{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap, colorConverter, LinearSegmentedColormap\n",
    "def discrete_scatter(x1, x2, y=None, markers=None, s=10, ax=None,\n",
    "                     labels=None, padding=.2, alpha=1, c=None, markeredgewidth=None):\n",
    "    \"\"\"Adaption of matplotlib.pyplot.scatter to plot classes or clusters.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    x1 : nd-array\n",
    "        input data, first axis\n",
    "\n",
    "    x2 : nd-array\n",
    "        input data, second axis\n",
    "\n",
    "    y : nd-array\n",
    "        input data, discrete labels\n",
    "\n",
    "    cmap : colormap\n",
    "        Colormap to use.\n",
    "\n",
    "    markers : list of string\n",
    "        List of markers to use, or None (which defaults to 'o').\n",
    "\n",
    "    s : int or float\n",
    "        Size of the marker\n",
    "\n",
    "    padding : float\n",
    "        Fraction of the dataset range to use for padding the axes.\n",
    "\n",
    "    alpha : float\n",
    "        Alpha value for all points.\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    if y is None:\n",
    "        y = np.zeros(len(x1))\n",
    "\n",
    "    unique_y = np.unique(y)\n",
    "\n",
    "    if markers is None:\n",
    "        markers = ['o', '^', 'v', 'D', 's', '*', 'p', 'h', 'H', '8', '<', '>'] * 10\n",
    "\n",
    "    if len(markers) == 1:\n",
    "        markers = markers * len(unique_y)\n",
    "\n",
    "    if labels is None:\n",
    "        labels = unique_y\n",
    "\n",
    "    # lines in the matplotlib sense, not actual lines\n",
    "    lines = []\n",
    "\n",
    "    current_cycler = mpl.rcParams['axes.prop_cycle']\n",
    "\n",
    "    for i, (yy, cycle) in enumerate(zip(unique_y, current_cycler())):\n",
    "        mask = y == yy\n",
    "        # if c is none, use color cycle\n",
    "        if c is None:\n",
    "            color = cycle['color']\n",
    "        elif len(c) > 1:\n",
    "            color = c[i]\n",
    "        else:\n",
    "            color = c\n",
    "        # use light edge for dark markers\n",
    "        if np.mean(colorConverter.to_rgb(color)) < .4:\n",
    "            markeredgecolor = \"grey\"\n",
    "        else:\n",
    "            markeredgecolor = \"black\"\n",
    "\n",
    "        lines.append(ax.plot(x1[mask], x2[mask], markers[i], markersize=s,\n",
    "                             label=labels[i], alpha=alpha, c=color,\n",
    "                             markeredgewidth=markeredgewidth,\n",
    "                             markeredgecolor=markeredgecolor)[0])\n",
    "\n",
    "    if padding != 0:\n",
    "        pad1 = x1.std() * padding\n",
    "        pad2 = x2.std() * padding\n",
    "        xlim = ax.get_xlim()\n",
    "        ylim = ax.get_ylim()\n",
    "        ax.set_xlim(min(x1.min() - pad1, xlim[0]), max(x1.max() + pad1, xlim[1]))\n",
    "        ax.set_ylim(min(x2.min() - pad2, ylim[0]), max(x2.max() + pad2, ylim[1]))\n",
    "\n",
    "    return lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 此处可以输入markdown文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__globals__', 'heightWeightData', '__version__', '__header__']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.io as sio\n",
    "data=sio.loadmat('heightWeight.mat')\n",
    "list(data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(210, 3)\n"
     ]
    }
   ],
   "source": [
    "heightweight=data['heightWeightData']\n",
    "print(heightweight.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=heightweight[:,0]   # 标签值所在的列\n",
    "plt.rcParams['figure.figsize'] = (15.0, 5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1717f710>,\n",
       " <matplotlib.lines.Line2D at 0x1717f978>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3EAAAEyCAYAAABUJ1mnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xt4VNW5P/DvmslkCLkDUULKJY20YgIEE1rPMai1YI0abAtSIIl4wdafiKDUWw5YFQserYhAq6eCF0y4FQWJEksAa4EqQkwgidjqCAphuOVKIExmMuv3RzKQZGZ2krnvzPfzPHnEvWfNXnsPYeXNWut9hZQSREREREREpA4af3eAiIiIiIiIuo9BHBERERERkYowiCMiIiIiIlIRBnFEREREREQqwiCOiIiIiIhIRRjEERERERERqQiDOCIiIiIiIhVhEEdERERERKQiDOKIiIiIiIhUJMTfHQCAAQMGyGHDhvm7G0RE5AMlJSVnpJRx/u6HWnCMJCIKDj0ZHwMiiBs2bBj279/v724QEZEPCCG+83cf1IRjJBFRcOjJ+MjllERERERERCrCII6IiIiIiEhFGMQRERERERGpCIM4IiIiIiIiFWEQR0REREREpCIM4oiIiIiIejGDwYBZs+cgpn8cNFotYvrHYdbsOTAYDF5pR94XECUGiIjcZbVKbDlwHKt2H4axvgnx0WG4NyMRE0cPgkYjPN6OiIhIDYqKijBlWjb0KTchfPJiREdfBkv9Kawv3Y7VaWOxYW0BMjMzPdaOfENIKf3dB6Snp0vWwCEiV1mtEvfnl2D3N2dwvrnl4vEwnRbjhg/AazlpDgMyV9uRe4QQJVLKdH/3Qy04RhKRqwwGA1LTxiIiKw/6hBF2501Vh9BYuAhlJfuQlJTkdjtyT0/GRy6nJCLV23LguF0gBgBN5hbs+voMCg8e92g7IiIiNViydBn0KTc5DMQAQJ8wAvrkCXj5leUeaUe+wyCOiFRv1e7DdoGYTZO5BSt3HfZoOyIiIjUoWLMG+uTxiq/Rp0xAfsEaj7Qj32EQR0SqZ6xvcum8q+2IiIjUoKGuBiHRlym+JiQqDmfrajzSjnyHQRwRqV58dJhL511tR0REpAZRMf1gqT+l+BpLw2lExvTzSDvyHQZxRKR692YkIkyndXguTKfFzHGJHm1HRESkBtnTp8NUuV3xNaaKYuRkT/dIO/IdBnFEpHoTRw/CuOED7AIyW5bJrFGDPNqOiIhIDR6Z+xBMFdtgqjrk8Lyp6hBMlcV4eM5sj7Qj3+myTpwQYjCA1QAGArAC+KuU8hUhxHoAP257WQyAOillqhBiGIBDAP7ddu4zKeX9nu44EZGNRiPwWk4aCg8ex8pdl+q9zRyXiKxRzuu9udqOCOD4SESBLykpCRvWFmDKtGyYkydAnzIBIVFxsDSchqmiGKbKYmxYW2BXJsDVduQ7XdaJE0LEA4iXUn4hhIgEUALgl1LKL9u95iUA9VLKZ9sGqQ+klCnd7QRr4BARBY/eUifOF+MjwDGSiNxnMBjw8ivLkV+wBmfrahAZ0w852dPx8JzZioGYq+3INT0ZH3tc7FsI8T6AFVLK4rb/FwC+B3CjlPJrBnFERKSktwRxnXljfAQ4RhIRBQuvFftuG4DGANjb7vA4ACellF+3O5YohCgVQnwihBjn5L1+K4TYL4TYf/r06Z50g4iIKKB4cnxsez+OkURE5FS3gzghRASAdwHMlVI2tDs1DcDadv9vBDBESjkGwCMA1gghojq/n5Tyr1LKdCllelxcnGu9JyIi8jNPj48Ax0giIlLWrSBOCKFD6wBVIKV8r93xEAC/BrDedkxKaZJSVrf9uQSAAcCPPNlpIiKiQMDxkYiI/KHLIK5tTf8qAIeklEs6nR4P4Csp5bF2r48TQmjb/vxDAMMBfOu5LhMREfkfx0ciIvKX7szEXQsgF8CNQoiytq9b2s5NRcelIgBwHYCDQogDADYCuF9KWeOxHhMREQUGjo9EROQXXdaJk1LuBuCwWJKU8i4Hx95F69ISIiKiXovjIxER+UuPslMSERERERGRfzGIIyIiIiIiUhEGcURERERERCrCII6IiIiIiEhFGMQRERERERGpCIM4IiIiIiIiFWEQR0REREREpCIM4oiIiIiIiFSEQRwREREREZGKMIgjIiIiIiJSEQZxREREREREKsIgjoiIiIiISEUYxBEREREREakIgzgiIiIiIiIVYRBHRERERESkIgziiIiIiIiIVIRBHBERERERkYowiCMiIiIiIlIRBnFEREREREQqwiCOiIiIiIhIRRjEERERERERqQiDOCIiIiIiIhVhEEdERERE1IsZDAbMmj0HMf3joNFqEdM/DrNmz4HBYPB318hFIf7uABGRJ1itElsOHMeq3YdhrG9CfHQY7s1IxMTRg6DRCH93j4iIyC+KioowZVo29Ck3IXzyYkRHXwZL/SmsL92O1WljsWFtATIzM/3dTeqhLmfihBCDhRAfCyEOCSEqhRBz2o4/LYSoEkKUtX3d0q7Nk0KIb4QQ/xZC/MKbN0BEZLVK3J9fgrxN5SivqseZxmaUV9XjyffKcX9+CaxW6e8uUi/FMZKIApnBYMCUadmIyMpDREYudLHxEBotdLHxiMjIRURWHqZMy+aMnAp1ZzmlBcA8KeUIANcAmCWEuKrt3MtSytS2r60A0HZuKoBkADcD+IsQQuuFvhMRAQC2HDiO3d+cwfnmlg7Hm8wt2PX1GRQePO6nnlEQ4BhJRAFrydJl0KfcBH3CCIfn9QkjoE+egJdfWe7jnpG7ugzipJRGKeUXbX8+C+AQgASFJrcDWCelNEkpDwP4BsBPPNFZIiJHVu0+bBfA2TSZW7By12Ef94iCBcdIIgpkBWvWQJ88HuZaI2p2vI6jy3Pw3QsTcXR5Dmp2vA5zrRH6lAnIL1jj765SD/UosYkQYhiAMQD2th16UAhxUAjxhhAitu1YAoCj7Zodg4MBTQjxWyHEfiHE/tOnT/e440RENsb6JrfOE3kCx0giCjQNdTUwVx/FiXfmQYSEYmDOixjy+00YmPMiREgoTrwzD+Yz3+NsXY2/u0o91O0gTggRAeBdAHOllA0AXgWQBCAVgBHAS7aXOmhutyFFSvlXKWW6lDI9Li6uxx0nIrIZGNXHrfNE7uIYSUSBKDwyGtUfvozLJi1A7PUzOuyJi71+Bi6btADVW5eib2S0v7tKPdStIE4IoUPr4FQgpXwPAKSUJ6WULVJKK4DXcWk5yDEAg9s1/wEAbkghIq9JHRyjeH7MkFjF80Tu4BhJRIFq2LBERIxW3hMXMWoCEhOH+bJbXhFsZRS6k51SAFgF4JCUckm74/HtXvYrABVtf94CYKoQQi+ESAQwHMDnnusyEVFHZUfrFM+XHq31UU+8z2qV2Fxahazlu5H+XDGylu/G5tIqZuD0E46RRBTIvvvuCCJG36z4mojUTBw58p2PeuQdRUVFSE0bi/WlJxE+eTEGz9uE8MmLsb70JFLTxqKoqMjfXfS47tSJuxZALoByIURZ27E8ANOEEKloXQZyBMDvAEBKWSmE2ADgS7Rm7ZolpXSccYCIyANONFxQPl+vfF4tbKUU2mfiPNPYjCffK8fWciNey0ljTTzf4xhJRAGrsaEOsdGXKb4mJCoO5xqUfxkayNqXUWg/46iLjYcuIxe6xHRMmZaNspJ9SEpK8mNPPavLIE5KuRuO1/BvVWjzRwB/dKNfRETdFh8dhjONzYrne4PulFK4PVUpMSJ5GsdIIgpkUTH9YKk/BV1svNPXWBpOIzKmnw975VndKaNgbiujsGLZUh/3znt6lJ2SiCgQ3ZuRiDCd41JbYTotZo5L9HGPvIOlFIiIqCeyp0+HqXK74mtMFcXIyZ7uox55nq2MgpLeWEaBQRwRqd7E0YMwbvgAu0AuTKfFuOEDkDVqkJ965lkspUBERD3xyNyHYKrYBlPVIYfnTVWHYKosxsNzZvu4Z57TUFeDkG4sGe1tZRS6syeOiCigaTQCr+WkofDgcazcdRjG+ibER4dh5rhEZI0a1Gv2iQXLslEiIvKMpKQkbFhbgCnTsmFOngB9ygSERMXB0nAapopimCqLsWFtgar3igXDklFHOBNHRL2CRiNwe2oCCmdnYP/8CSicnYHbUxN6TQAHBM+yUSIics5oNOLmn1+PEydOdOv1mZmZKCvZh6lp8Ti3MQ/HlkzCuY15mJoWj7KSfcjMzPR4H32Z7j8Ylow6IqT0f1rq9PR0uX//fn93g4gCgNUqseXAcazafWlG7d6MREwcHXgzar7uqy075a6vz6DJfGlvnG3ZqFqyUwohSqSU6f7uh1pwjCSi9h6e/QDeXvVX3DXzd1iy7M/+7o6doqIiTJmW3ZpsJHk8QqIvg6X+FEyV22Gq2IYNaws8GjgaDAakpo21y05pY6o6hMbCRarITtmT8ZFBHBEFDEcp9IHADFL81VerVap+2SiDuJ7hGElENkajEck/TsKOaRqMX2dF5b+/xcCBA/3drYv8FVBdDBwVlox6Y8bR03oyPnI5JREFjO6k0A8U/uprMCwbJSIix15YtBAzRmkxJl6LO0dq8cKihf7uUgfdSfevb0v370n+WDLqb5yJI6KAkbV8N8qr6p2eH5kQjcLZGT7skXNq6mug4Uxcz3CMJCLg0ixc5X1axEdqYDxrRcrKloCajYvpH4fwyYsVk4yYa404tzEPddWnfNgzdeBMHBGpkppS6Kupr0REpH62Wbj4yNYf3+MjNQE3Gxes6f79gUEcEQWMrlLkB1IKfTX1lYiI1M1oNOLtt9/CYz/tePyxnwJvv/1mtzNVepst3b+S3pju3x8YxBFRwFBTCn019ZWIiNSt8yycTaDNxgVrun9/YBBHRAFj4uhBGDd8gF1wZMv4mDVqkJ96Zk9NfSUiIvVyNgtnE0izcY/MfQimim0wVR1yeN5UdQimymI8PGe2j3vW+4T4uwNERDYajcBrOWmqSKGvpr4SEZF6OZuFs2mdjWt9nb/rxiUlJWHD2gJMmZYNs0K6/0Cv16YGDOKIKKDYUujfnprg7650SU19JSIiddq391Ps2deIpXuUX3dt879806Eu2NL9v/zKcuQX5OFsXQ0iY/ohJ3s6Hl4d+AW31YIlBoiIyKdYYqBnOEYSEQUHlhggIiIiIiLqpRjEERERERERqQiDOCIiIiIiIhVhEEdERERERKQiDOKIiIiIiIhUhEEcERERERGRijCIIyIiIiIiUhEGcURERERERCrCII6IiIiIiDzGYDBg1uw5iOkfB41Wi5j+cZg1ew4MBoO/u9ZrhPi7A0REamW1Smw5cByrdh+Gsb4J8dFhuDcjERNHD4JGI/zdPSIiIp8rKirClGnZ0KfchPDJixEdfRks9aewvnQ7VqeNxYa1BcjMzPR3N1WvyyBOCDEYwGoAAwFYAfxVSvmKEOJFAFkAmgEYANwtpawTQgwDcAjAv9ve4jMp5f1e6DsRkd9YrRL355dg9zdncL65BQBwprEZT75Xjq3lRryWk8ZArpfj+EhE1JHBYMCUadmIyMqDPmHExeO62HjoMnKhS0zHlGnZKCvZh6SkJD/2VP26s5zSAmCelHIEgGsAzBJCXAWgGECKlHIUgP8AeLJdG4OUMrXtiwMUEfU6Ww4c7xDA2TSZW7Dr6zMoPHjcTz0jH+L4SETUzpKly6BPualDANeePmEE9MkT8PIry33cs96nyyBOSmmUUn7R9uezaP0tYoKUcpuU0tL2ss8A/MB73SQiCiyrdh+2C+BsmswtWLnrsI97RL7G8ZGIqKOCNWugTx6v+Bp9ygTkF6zxUY96rx4lNmlbCjIGwN5Op+4BUNTu/xOFEKVCiE+EEOOcvNdvhRD7hRD7T58+3ZNuEBH5nbG+ya3z1Lt4cnxsez+OkUSkOg11NQiJvkzxNSFRcThbV+OjHvVe3Q7ihBARAN4FMFdK2dDu+P+gdUlJQdshI4AhUsoxAB4BsEYIEdX5/aSUf5VSpksp0+Pi4ty5ByIin4uPDnPrPPUenh4fAY6RRKROUTH9YKk/pfgaS8NpRMb081GPeq9uBXFCCB1aB6gCKeV77Y7PAHAbgGwppQQAKaVJSlnd9ucStG7q/pGnO05E5E/3ZiQiTKd1eC5Mp8XMcYk+7hH5A8dHIqJLsqdPh6lyu+JrTBXFyMme7qMe9V5dBnFCCAFgFYBDUsol7Y7fDOBxABOllOfbHY8TQmjb/vxDAMMBfOvpjhMR+dPE0YMwbvgAu0AuTKfFuOEDkDVqkJ96Rr7C8ZGIqKNH5j4EU8U2mKoOOTxvqjoEU2UxHp4z28c96326UyfuWgC5AMqFEGVtx/IALAOgB1DcOo5dTJV8HYBnhRAWAC0A7pdScuErEfUqGo3AazlpKDx4HCt3XaoTN3NcIrJGsU5ckOD4SETUTlJSEjasLcCUadkwJ0+APmUCQqLiYGk4DVNFMUyVxdiwtoDlBTxAtK3y8Kv09HS5f/9+f3eDiIh8QAhRIqVM93c/1IJjJBGpjcFgwMuvLEd+wRqcratBZEw/5GRPx8NzZjOAU9CT8bE7M3FERERERETdkpSUhBXLlmLFsqX+7kqv1aMSA0RERERERORfDOKIiIiIiAKcwWDArNlzENM/DhqtFjH94zBr9hwYDAZ/d438gMspiSigWK0SWw4cx6rdl5KF3JuRiImjmSyEiIiCU1FREaZMy4Y+5SaET16M6OjLYKk/hfWl27E6bSw2rC1AZmamv7tJPsQgjogChtUqcX9+CXZ/cwbnm1sAAGcam/Hke+XYWm7EazlpDOSIiCioGAwGTJmWjYisPOgTRlw8rouNhy4jF7rEdEyZlo2ykn1MGhJEuJySiALGlgPHOwRwNk3mFuz6+gwKDx73U8+IiIj8Y8nSZdCn3NQhgGtPnzAC+uQJePmV5T7uGfkTgzgiChirdh+2C+BsmswtWLnrsI97RERE5F8Fa9ZAnzxe8TX6lAnIL1jjox5RIGAQR0QBw1jf5NZ5IiKi3qahrgYh0ZcpviYkKg5n62p81CMKBAziiChgxEeHuXWeiIiot4mK6QdL/SnF11gaTiMypp+PekSBgEEcEQWMezMSEabTOjwXptNi5rhEH/eIiIjIv7KnT4epcrvia0wVxcjJnu6jHpGNP8s+MIgjooAxcfQgZFwxAKHajv80hWo1GDd8ALJGDfJTz4KX1SqxubQKWct3I/25YmQt343NpVWwWqW/u0ZEFBQemfsQzh0ogqnqkMPzpqpDOHfwIzw8Z7aPexbcioqKkJo2FutLTyJ88mIMnrcJ4ZMXY33pSaSmjUVRUZFXr88SA0QUYGTbV/sjElIyaPA1lnwgIgoQVitObXwGEak3I2LULxASFQdLw2k0Hvw7Gss+QiinZXwqEMo+8CMnooCx5cBx7DFUo7mlY8BmbpHY/U01Swz4GEs+EBH535KlyxA+5lYMvPNlSIsZJwoexfcv/RonCh6FtJgx8M6XEZ56C0sM+FAglH0QgfDb7fT0dLl//35/d4OI/Cxr+W6UV9U7PT8yIRqFszN82KPg5q3PQwhRIqVMd6dvwYRjJFFwi+kfh/DJi6GLjXf6GnOtEec25qGuWjkBCnmGtz6TnoyPnIkjooDBEgOBhZ8HEZH/scRA4AmEz4RBHBEFDJYYCCz8PIiI/I8lBgJPIHwmDOKIKGCwxEBg4edBROR/nigx4M9U+L1RIJR9YBBHRAFj4uhBGDd8gF3gEKbTdlligKnwPc+dz4OIiDzjkbkPwVSxTbHEgKmy2GmJAX+nwu+N3P1MPIGJTYgooFitEoUHj2PlrsMw1jchPjoMM8clImvUIKfp7B2lwgcuBRtMhe86Vz6PrjCxSc9wjCSioqIiTJmWDX3yBOhTJlwsMWCqKIapshgb1hYgMzPTrp3BYEBq2li7VPg2pqpDaCxc5NVU+L2Vq5+Jkp6MjwziiEj1NpdWIW9TuV0qfKA1kHt+0kjcnprgh56RIwzieoZjJBEBrQHZy68sR37BGpytq0FkTD/kZE/Hw3NmOw3AZs2eg/WlJxGRkev0fRt3rcbUtHisWLbUW13vtVz5TJQwiCOioMLSBOrCIK5nOEYSkatYnkBdWGKAiIIKU+ETERHZC4RU+OQdDOKISPWYCp+IiMiev1LhMxum9zGIIyLV81cqfGbEJCKiQOaPVPjMhukbXQZxQojBQoiPhRCHhBCVQog5bcf7CSGKhRBft/03tu24EEIsE0J8I4Q4KIS42ts3QUTBzR+p8G0ZMfM2laO8qh5nGptRXlWPJ98rx/35JQzkggTHSCIKZL5OhW8wGDBlWjYisvIQkZELXWw8hEYLXWw8IjJyEZGVhynTsjkj5wHdmYmzAJgnpRwB4BoAs4QQVwF4AsAOKeVwADva/h8AMgEMb/v6LYBXPd5rIqJ2NBqB13LS8PykkRiZEI0BEaEYmRCN5yeN9Fp5gS0HjtuVNACAJnMLdn19BoUHj3v8mhSQOEYSUcBKSkrChrUFaCxchMZdq2GuNUK2WGCuNaJx12o0Fi7ChrUFHisvsGTpMuhTbnJYzgAA9AkjoE+egJdfWe6R6wWzHmenFEK8D2BF29cNUkqjECIewD+klD8WQvxf25/Xtr3+37bXOXtPZt4iIrVhRkzX9ebslBwjiSgQeToVvjPMhumenoyPIT1842EAxgDYC+By26DTNkjZUt8kADjartmxtmMdBighxG/R+ltIDBkypCfdICLyO2bEpM44RhJRoEpKSsKKZUu9Xguuoa4G0cyG6RPdTmwihIgA8C6AuVLKBqWXOjhmN90npfyrlDJdSpkeFxfX3W4QEQUEZsSk9jhGEhH5LxtmMOpWECeE0KF1cCqQUr7Xdvhk2xIRtP3X9okdAzC4XfMfAODmECLqVfyVEZMCD8dIouDDFPqO+SMbZrDqTnZKAWAVgENSyiXtTm0BMKPtzzMAvN/u+J1tGbiuAVCvtNafiAIXU+g754+MmBR4OEYSBR+m0HfO19kwg1mXiU2EEBkAdgEoB2BtO5yH1jX/GwAMAfA9gDuklDVtA9oKADcDOA/gbiml4o5sbtomCjy2FPqdMzDaghRvZX1UE6tVovDgcazcdRjG+ibER4dh5rhEZI0aFPTPRklvSmzCMZIouBgMBqSmjUVEVp7DDIymqkNoLFyEspJ9Hk0YoiZFRUWYMi0b+uQJ0KdMQEhUHCwNp2GqKIapshgb1hYgMzPT390MSD0ZH3ucndIbOEARBZ7NpVXI21Rul0IfaA3knp80ErenJvihZ6R2vSmI8wWOkUTeYzAYsGTpMhSsWYOGuhpExfRD9vTpeGTuQw6DsFmz52B96UlEZOQ6fc/GXasxNS3e60lEApmvsmH2NgziiOgiq1Viy4HjWLX70mzRvRmJmDhaebaIKfTJWxjE9QzHSCLvuDhjlHIT9MnjERJ9GSz1p2Cq3A5TxTaHM0ZMoU/e5LUSA0SkLo6WRJ5pbMaT75Vja7lRcUkkU+gTEVFvZTAYMGVatt2ySF1sPHQZudAlpmPKtGy7ZZFMoU+BotslBohIfbYcOG63pw0Amswt2PX1GRQedJ4Ujyn0iYiot1qydFnrDJyDfW0AoE8YAX3yBLz8yvIOx5lCnwIFgziiXmzV7sMO97QBrYHcyl2HnbZlCn0iIlKLnqb8L1izBvrk8YrvqU+ZgPyCNR2OBVsK/WAppaDG++RySiKVcGVvmztLIieOHoSt5Ubs+voMmsz22Sl7Swp9V/cMqk2w3CcRBZ/2e9vCJy9GdNvetvWl27E6bazDvW2uLot8ZO5DWJ02FrrEdKfZKU2VxXh49T73b8zPXHmuaqTW+2RiEyIVcDXdv7vJSXp7Cv1gKaMQaPfJxCY9wzGSyDlXU/67k6AkGFLoB0sphUC7z56Mj1xOSaQCru5tc3dJpEYjcHtqAgpnZ2D//AkonJ2B21MTekVgA7i3Z1BNguU+iSj4uLq3zZ1lkZmZmSgr2YepafE4tzEPx5ZMwrmNeZiaFo+ykn2qD+AA15+r2qj5PhnEEamAq3vbJo4ehHHDB9gFcr1tSaSr3NkzqCbBcp9EFHxc3dv2yNyHYKrYBlPVIYdtLi6LnDPb4fmkpCSsWLYUddWn0NJiQV31KaxYtlTVs1Ltufpc1UbN98k9cUQq4OreNo1G4LWctF69JNIdwVJGIVjuk4iCj6t725KSkrBhbQGmTMuGWWFZZG8JynoqWEopqPk+ORNHpALupPvv7Usi3REsZRSC5T6JKPi4k/LfnWWRO3fuxMjUNGhC+0AIDTShfTAyNQ07d+7sss9qyITobikFd+7Rl89HzSUjGMQRqYC/0v1bLFbM31SOKxcUIfGJD3HlgiLM31QOi8Wq2M5qldhcWoWs5buR/lwxspbvxubSKlit/k+k1J67z9XV+/T182G5CCLqrdxN+e/Ksshnn30W42++Bd/3SUT83Ssw5NHNiL97Bb7vk4jxN9+CZ5991mnboqIipKaNxfrSkwifvBiD521C+OTFWF96EqlpY1FUVNS9G/cyd56rO/fo6+ej5pIRzE5JpAK27ILO0v17I7ugxWLFfz2/E6cbTXbn4iL1+PTxGxESYv97oEDLhKjEnefq6n364/n44++PEman7BmOkUTO+Tq74M6dOzH+5ltw+W+ec3q9k+vnY/tHW3HjjTf6ta/ucLWv7tyjP55PoH0mzE5J1MvY9rY9P2kkRiZEY0BEKEYmROP5SSO99gP404WVDgM4ADh91oRnPvjS4Tk1ZUJ057m6ep/+eD7++PtDROQLtr1tjYWL0LhrNcy1RsgWC8y1RjTuWo3GwkUe3ds255FHEXn1bYrZDCPH3Iq58x61O6emTIiuPld37tEfz8fXf388iTNxROTQlQuKcMHsfNlkH50GXy203y/gbm06tXD1PoPl+SjhTFzPcIwk6prBYMDLryxHfsEanK2rQWRMP+RkT8fDc2Z79AdwTWgfxN+9osv6csY3H4S1+UKH4+7UpgNa73HJ0mUoWLMGDXU1iIrph+zp0/HI3Ie8FmT09Lm6c4/uPh93+OrvT1d6Mj4yOyUROWRSCOCUzrubCdFqldhy4DgMRE1WAAAgAElEQVRW7b6UTfPejERMHB1Y2TRdvU9miiQi8jzb3rYVy5Z69TrS3IyQbmQzlOZmu+PuZEK8WGA85SaET16M6OjLYKk/hfWl27E6bazXCoz39Lm6c4/+zBTpq78/nsTllETkkF6n/M+Ds/PuZEK07d3K21SO8qp6nGlsRnlVPZ58rxz355cEVGIUV++TmSKJiNRL6EK7lc1Q6ELtjruaCdFgMGDKtGxEZOUhIiMXuth4CI0Wuth4RGTkIiIrD1OmZQdEdkt3sj2qOVOkPzCIIyKHJl/9A8Xzd6QNdnjcnUyIatpP5+p9MlMkEVHg6Gk6++SrktF44CPF92wsK0JKcrLdcVczIfpzL11PSynY7tFca0TNjtdxdHkOvnthIo4uz0HNjtdhrjU6zfaoxkyR/iwXwT1xRL2cq8sT3c1O6UomRH/sF7NYrHi6sBIbvzgGk9kKvU6DyVf/AE9nJTu8PxurVeJ37+zHP/5zGuaWS/+O6rQCN/woDv+Xm66YnTJQMkV2xRvLW7knrmc4RhJ5R/slivrk8QhpW6JoqtwOU8U2h0sU/ZGd0l97xZ599lk8/dwiRF59GyJG33zx+TQe+Ahnv/gAT8/Pw1NPPWV3jymjUtFsBSJSMxEx6qZL7Q5uQ2NZEUI1QMXBsoDITukOV/7+dKUn4yODOKJezN109haLFc988CX+VnL0YoBzR9pg/OG2q7oMcAoPHsfKXZd+8J85LhFZo5R/8E9/rhhnGu33EdgMiAjF/vkTurjr7nM1UAVsQVwJPvnPaTS3XNofGKrV4IYfx3VZnsCV5+Nr3iqHwCCuZzhGEnmeOwHDxeBmzK2ISM1ESFQcLA2n0VhWhLOlHzoMbmwu/uCfPAH6lAkX25oqimGqLHb4g79Gq8XgeZsgNI5XcQCAbLHg2JJJaGmxuPA07LkarBoMBqSkXo2YXy5w2q5u80JUlH3hMBBz5fn4g7cCTgZxRAQA2FxahbxN5XbLE4HWH8SfnzQSt6cm+KFnjvl6Jm7+pnLk7/3e6fnca4Zi4S9THJ5T27N1hbfukUFcz3CMJPK8WbPnYH3pSURk5Dp9TeOu1ZiaFu8w2cXOnTsxd96jqKishDQ3Q+hCkZKcjKUvvWg3A+eo7Zx5j6KyXdvk5GS84qStJ2bieprZcmRqGr7vk4jYG+52es3aj9/A0OYjOFhacvGYu8/V1tdAyBSpxBP36QiDOCIC4L909q4uwdtcWoUn3yvvsMzQpqugwZVrulpGAQiOUgHeukcGcT3DMZLI8/y1RNGVJXizZs/BuhIjIq+b4fR9G/75FqanJzgMGFy5pqulFPxZJsCXvHWfLPZNRAD8k87enQyTt42MR4TeceWTiD4huDXF8T+Wrl7T1TIKQHCUCgiGeySi4NRQV9OtUgGeTGfvapbJSb+6HbX7t8BUdcjh+5qqDqFufyF+/cuJHrumq6UU/PFc/SEQ7pNBHFEv5o909u5kmPyg3IhGk9nhubNNZnxYYfToNV0towAER6mAYLhHIgpO/khn72qWyXc3vY+I4T/FqXcXovaTt2CuNUK2WGCuNaL2k7dw6t2FiLjiJ3hv8xaPXdPVUgrBUiYgEO6TQRxRL+aPdPardh92uIcKaA2qVu46rNi2ycns1wWL1WlbV6/pahkFIDhKBQTDPRJRcOqczt7SWIP6DU+gpbH24jFPp7MvWLMG+uTxiq/Rp0xAfsEau3aRGbkYmPsSpMWMEwWP4vuXfo0TBY9CWswYmPsSIsfdadfOnWu6WkpBjWUCXBEI99llECeEeEMIcUoIUdHu2HohRFnb1xEhRFnb8WFCiKZ2517zWs+JqEsTRw9CxhUDEKrt+K0eqtVg3PAByBo1yOPXdGcJnqttXW33dFYy4iL0Ds/FRerxh9uucvqeE0cPwrjhA+yCHFvmRm88W18Lhnt0F8dIInV6ZO5DMFVsu7hE8cLedZDGL9G0dx2A1iWKpspiPDxntseu6eoSPFs7XWw8+v38Pgx+MB9DH9uCwQ/mo9/P74MuNt7p0j1Xr/nKkhdx9osPFJdwni39EEtferHD8c7P1VE7Tz9XfwiE+3S8+aSjtwCsALDadkBK+Rvbn4UQLwFov/PdIKVM9VQHichdsu2r/REJbyU1GhjVR7FMwMCoPk7PxUeHKbZ1tnzP1XYhIRp8+sSNLpVR0GgEXstJU0WpAFcFwz16wFvgGEmkOklJSdiwtgBTpmXDNDwDF8qLsWdGGDJWF8MKDcxf78aGtQUezYbYNyIKlvpTiskwLA2nERYZ1eGYbeleV+0cLd1zte2NN96Ip+fn4enn5iuWUuicTbP9czUrlAkIlCyTrgqE++wyiJNS/lMIMczROSGEADAFgHIuVSLyiy0HjmOPoRrNLR0DNnOLxO5vqlF48LjH0+BH9VH+ZyU6TOf03L0ZiYrZKZ0t33O1HdAayC38ZYrTUgJKNBqB21MTVF9KQEkw3KM7OEYSqVdmZibKSvbhN5N/hZ+MFBgTr8WMFDM+t3yH9V4oKB2iC8XZ0q3od+O9Tl9ztnQrdCEdx8ns6dOxvnQ7dArp7J0t3XOn7VNPPYWMjIzWUgpvPtixlIKDYuY2tufaWiYgr2OZgNWBUajbE/x9n+7uiRsH4KSU8ut2xxKFEKVCiE+EEOPcfH8icoM7+9Nc9fkR5UxMew9XOz3n6vI9LvujAMUxkijA9e3bF98avsGC61oTdCy4LhSHv/0a4eHhHr9WfV0dGg/+XXEJXuOBv6Ourq7DcXeW7rm77G/o0KEYl5GBqMhICI1AVGQkxmVkYOjQoUq3iqSkJKxYthR11afQ0mJBXfUprFi2tNcEcDb+vM/uLKdUMg3A2nb/bwQwREpZLYRIA7BZCJEspWzo3FAI8VsAvwWAIUOGuNkNInLEHyniLcpZ+xXPu7p8z51lf67WtCPqBo6RRAHuhUULMWOUFvGRrfMa8ZEa3Dmy9fiSZX/26LWEAGJ/NrM1m+TomxAx6heXlige/DsaD2xD7I0zUbu943ZZd5buudO2fX258MmLEd1WX2596XasThvrsL4c+U63in23LRX5QEqZ0u5YCIAqAGlSymNO2v0DwO+llIpVSlnIlMg7/FGQetgTH3b5miPP3+rRa7rKVl+uc3kC2wzeazlpDOS8oLcV++YYSeRZRqMRd+dMxVsF6zFw4ECvXueqH/0QX/425GIQBwDGs1Yk/9WCL78+7NHrC6HBkEc348LRCtT9402YzxyFtDRDhIRCN2AwYm64G31+kIzv//QrSGn/G0+DwdC2dG9Nx6V7c2Z3OfPT07YGgwGpaWMRkZXnsDyBqeoQGgsXocwLy06Dma+KfY8H8FX7wUkIESeE0Lb9+YcAhgP41o1rEPU6VqvE5tIqZC3fjfTnipG1fDc2l1YpFsF2lT9SxPcLd77nrfV8qOJ5X3Knph1RFzhGErnohUUL8fmne/DCooVevc6s++/DtCutHQI4oHU2buqVVsy6f6ZHr6cN1aOxfAfOvP+/6DNsDOLvWYEhj25G/D0r0GfYGJx5/3/RWLETWr3jBGDuLN3raVtX68uR73SnxMBaAJ8C+LEQ4pgQwrYbcyo6LhMBgOsAHBRCHACwEcD9Ukp1l2Qn8iDbzE/epnKUV9XjTGMzyqvq8eR75bg/v8TjgdwtyQPR4uQ9W6wSmVd5/jecT958peL5vEzl877kjz2D1LtwjCTyLKPRiLfffgs7cvvg7bffxIkTJ7xynU8//RQfbd16cS9cZwuuC0XR1q347LPPPHbNG66/DrU7V+KySQsQe/0M6GLjITRa6GLjEXv9DFw2aQFqd67Ez66/zmPXdJWr9eXId7oM4qSU06SU8VJKnZTyB1LKVW3H75JSvtbpte9KKZOllKOllFdLKQu91XEiNfL1zM+zH36J5hbHm9CaW6xYuNXxRmd3aLUaOFuBqBUC2gBanuiPPYPUu3CMJPIs2x61MfFa3DlS67XZuAcf+B3uGhNqNwtnEx+pwV2poXjw//3OY9ccGB+PiNG/UJzdihj9CwyMd14OwFdcrS9HvuNudkoi6gFfz/xs/MLhVpyL/lZy1KPXA4A39xyBswnFFinxxp4jHr+mq5zVj+vueSIi8hzbLNxjP239/8d+Cq/Nxn3z1SG8us8E8UyD069X95nw9VdfOmxvMBgwa/YcxPSPg0arRUz/OMyaPQcGg8HpNT/44ENEjrlFsV+RY25BYWHXe8u9zVZfTomz2nTkGwziiHzI1zM/JrNyqsiuzruyf09Ns1v+2DNIRESOOc4U6Z3ZuLMmK4Y8+j76p9+CB37aF/IPURe/HvhpX/RPvxVDfr8Zjc32411RURFS08ZifelJhE9ejMHzNiF88mKsLz2J1LSxKCoqcnhNNc1uZU+fDlPldsXXOKsvR77BII7Ih3w98xOiVV66qHTe1f17aprdYn05IqLA0HkWzsZbs3G6sL4wGf+DportmH9txx+H51+rQVPldphOfANdn45jlsFgwJRp2YjIykNERm6HfW0RGbmIyMrDlGnZDmfk1DS75W59OfI+BnFEPuTrmZ9IvXIpyMg+zs+7un9PTbNbtvpyz08aiZEJ0RgQEYqRCdF4ftJIlhcgooBjNBpx88+v91qyD3/qPAtn093ZONvyxqjY/gjTCUTF9u9yeeO5na/irtEhDq951ygtzu38Czr/urJz1kZLYw3qNzyBlsZaAMpZGzvPbnVuC3hvdis/Px/9Lx8EjU4PITTQ6PTof/kg5OfnO3y9rb5cY+EiNO5aDXOtEbLFAnOtEY27VqOxcJHT+nLBxJVltZ7SrTpx3sYaOAQER9Fl2+zWrq/PoMncs7pkFosVTxdWYuMXx2AyW6HXaTD56h/g6axkhIQ4/n3M1Qu3oeac2Wl/+ofrULLgJofnXK0xZ7VK/O6dEnzyn9MdkqqEajW44cdxXguOmptbcPfb+/AvQzUkAAHgv5P6480ZYxEa6jiopK554/uyt9WJ8zaOkWTz8OwH8Paqv+Kumb/zeCFqfzIajUj+cRIq77MP4oDWum0pK1tQ+e9vHdZta1+U2tpwEtav/wnN8OugibocpoptDotSCyHQJwT49qEIp9dMWn4OTWZ0qNkW0z8O4ZMXQxfbmnykccdfYKn4CCEpmYj4+f8DAJhrjTi3MQ911R1n3TrXXuvc1lu113Jzc1Gw7m+ITLsNEaNvRkhb0e7GAx/hbMkHyJ56B9555x2Hbd2pTdfbtf97p08ef/G5miq3O/1715WejI8M4iggBFPRZatVovDgcazcdemH4pnjEpE1yvkPxRaLFf/1/E6cbjTZnYuL1OPTx290GMi5U+w7beE2VLsQALYGcfvbgrhL/77otAI3/CgO/5eb7vHPsrm5BSnPbHOYiTNUq0HFH25iIOcCb31fMojrGY6RBFwKdHZM02D8OqvTgEaNHp79AFD6Dl4e73yB2MPbrRBX32kXvLYPjLTRl6N21UzsvlOHjHcs6HfPSljqTzgMjKLCw5CTAvwl03nt0geKmpFfATScu7SXW6PVYvC8TRAaLSyNNXbX00bEQrZYcGzJJLS0WOze0/aDv254Bi6Uf4Q9M0KRsdoMfcovYP56t0s/+CvJz8/HnXfPxOVTn3NatPvkuvlY/eZK5OTkeOy6vZ23iqH7qtg3kccEU9FljUbg9tQEFM7OwP75E1A4OwO3pyYo/jD8dGGlwwAOAE6fNeGZDxxnz3JnaWNYqPJSzL5Ozm85cBx7DNUdAjgAMLdI7P6m2iuf5d1v71MspXDPav4A7Ipg+r4kCnS+Sr3vD/v2foqlexoVM0Uu3dOIzz/7l13b9ssbL+xdh7tGh2BMvBZ3jdKiae86p8sb+0VH4tXPLyhnp/z8AvrFRHZo135fm6PrAcr72jIzM1FWsg8/tBzBXSMFxsRrMSMF+KHlO5SV7PNoAAcAc+Y9hsi02xTLGkRefSvmznvMo9ft7QKhGDqDOAoILLqszNVSAe4k7nB1rswfn+W/DNWK5/d8c8bj1wwG/L4kCgy+TL3vD7s/L4WUssuv3Z+X2rW1FaW2NNZ0SFJiS07S0ljrsCj1jl2fIiI6FgNzXsTQxz+w+xqY8yIiomOx45+fdmhn29emdL2u9rWdOnUK//7q0MVC4wuuC8V/vvoSp0+fdus5OlJbU42I0TcrviYiNRM1NcrjKHUUCMXQGcRRQFBTWnp/cLVUgDuJO86bHf/wbtPk5Lw/PsuuFoX7f9G4OvH7kigw+DL1vtrY0vbbZsXaPyPb7JijtP2uJu6wZW3snBTFdr3Gna8qZm0sKirChJ//DLkjO36eOSO1GH/jDU7LE7hKWszdKmsgLc63T5C9QCgXwSCOAoKa0tL7g16n/K2qdN6V5ZsAMMjFz2RgVB/Fdl2dd0VXs4a9Yzel7/H7ksj/fJ16X22iYvp1q1SAo+WNtqWNU9PicW5jHo4tmYRzG/MwNS3e6dLGpKQkvPbn5TB9/ZnD65m++RSv/Xm5w31QBoMBk38zDVarFQvG6TqcWzBOBymtmPybaR7NbChCdN0qayBCdIqvoY4CoVwEgzgKCGpKS+8Pk6/+geL5O9IGe/yarn4mqYNjFN93zJBYt/vW2X8n9Vc8f+0VAzx+zWDA70si/3M39X5vlz19Opo+/r+Ls2LGs1bcnH8OJxqtl2bjPn7V6fLGpKQkrFi2FHXVp9DSYkFd9SmsWLZUMRnF/s/24L6f9HX4mdw3ti9K9trv3QNa91GFRMXh7lSd47IGo3UIiRrgdB9VT8sEAEBsv/5oPPCR0/MA0FhWhH79lMfRnvJn6n1fCIRi6AziKCCw6LKyp7OSERfhOINWXEQo/nDbVR6/pqufSdnROsX3LT1aq3i+ubkF2a9/hsQnPsSwJz5E4hMfIvv1z9DsZG8WALw5Yyx0TmYWdVqBN+70TiJEq1Vic2kVspbvRvpzxchavhubS6ucFkJXG35fEvmXs1k4G87GATnTp8J80nBxVuyFPc34vKoFL+xpBtA6O2Y+9S1ypk/1yPVsn8kT1zgec564Rjj9TN7Jz4f5zHd2M3g286/VwFJ9FKvfsQ/KcnNzcefdM2FJ/G/E3/NnDHl0M+Lv+TMsif+NO++eidzcXIfv+cpLL+BsyQeKRbvPfvEhlr70grNb7rGioiKkpo3F+tKTCJ+8GIPnbUL45MVYX3oSqWljPb5k1B8CoRg6gzgKCCy6rEyjERj9gxhoRcfnoBUCqYNjvPJ8XP1MTjRcUHzfE/XOz9tKBexpq/UGtO5n22Oobi0h4CSQ02gEosMcZ8uM7qPzyvOxpd/P21SO8qp6nGlsRnlVPZ58rxz355f0ikCO35dE/uVsFs6Gs3HAhjXv4L6xYRdn4d4+0Iwdd4bj7QPmi7Nx96WHYcMax3XQesqdz6T5bA3ucjAL177tjNEhaD7bcR9Vfn4+Ctb9DZdPfQ6xN9wNXWw8hEYLXWw8Ym+4G5dPfQ4F6/7mcEZu0KBBkLIFpzY+g9pP3uqw96/2k7dwauMzkLIFgwZ55pdyBoMBU6ZlIyIrDxEZuR36GpGRi4isPEyZlq36GblAKIbOOnFEKrC5tAp5m8odZgoM02nx/KSRuD01wQ89s+dObbrs1z/DHoVMkxlXDED+TPtfSc/fVI78vd87bZd7zVAs/GWKQq97Tk2fSaBhnbie4RgZvDJ+MgZ79pV1+bprx6Y6zNwYDNo/o1AtcO8YHf5yaxge+LAJq0rNsP0T7aln5M5nEh2mQ8MF+9pxnUX1CUF906VEI/0vHwRL4n8j9oa7nbap/fgN6I58ijMnO5Z+GZmahu/7JCJi9M04+8UHOHfoE1jPN0DTNwrhI65H5NW3obGsCEObj+BgaUmXfevKrNlzsL70JCIyHM8MAkDjrtWYmhaPFcuWun09f/N0MXQW+ybqZdwJjHxtc2kVnnyv3GH2yq6Cm8QnPlTMJCkAHH7+VrvjVy4owgWFDJ59dBp8tdCztXfU9JkEGgZxPcMxkqhrtmLolfdpL87KpaxsCaii6K4GOBqdHvH3/Bm62Hin7cy1RhjfmAWruWNNWU1oH8TfvaLrtm8+CGuz8kqa7ojpH4fwyYu7vN65jXmoq1ZODBKMWOybqJdRU6p3d/ZRuVoqwNUSDO5Q02dCRNTb+boMgyuJO1zdR+VOmQBpbu5eW3Oz4mu6KxBS7wcLBnFEKuCvVO+uJO5wZx+Vq6UC3CnB4Cqm3yciCgzulGHYuXMnRqamQRPapzXjY2gfjExNw86dO522aZ+4o88tjyN66Aj0ueWJLhN32PZRNbz/R9T/480O+6jq//EmGt7/o8N9VO6UCRC60O611TlOntZTgZB6P1gwiCNSAXdSvVssVszfVI4rFxQh8YkPceWCIszfVA6LRXl2ymqV+N07+/HYxgMdEnc8uvEAfvfO/i4Td3Reqd2dlduulgpQUwkGIiLyLFfLMDz77LMYf/Mt+L5PIuLvXtGa8fHuFfi+TyLG33wLnn32Wbs2nRN3WL76GNJ4CJavPu5+4g5pxYVjlTiR/3t8/9KvcSL/97hwrBKQjsdld8oEJF+VjMYy5WyQjWVFSElOVnxNdwVC6v1gwSCOSAVcXaJosVjxX8/vRP7e73HBbIUEcMFsRf7e7/Ff/7tTMZDbXFaFHV+dQnNLx+jL3CKx46tTeP9AlcN27mRtfHPGWIRqHf+zFKrVOC0V8NStVym2W3DLCKfXdBXT7xMR+Z+rZRh27tyJp59bhMt/4yTj42+ew9PPLbKbkVuydBn0KTdBnzAClsYaNFVsx8d3hqGpcjtaGmuhTxgBffIEh7XebAFg1C8X4PKcP2Hw7AIMfWwLBs8uwOU5f0LULxc4DADdKRPw+O8fxtkvPuyy7WPzHnb8AHsoEFLvBwsGcUQq4OoSxacLK3G60eTw3OmzJjzzwZdOr7mk+D9wFm9ZJbBk238cntty4Dh2f3PGLmtjk7kFu74+g8KDxx22A4DQUC0q/nATMq4YcHHppEBrVsqKP9yE0FDHM19bK0/ASQwHjQCKvvR8DSWm3yci8j9XU/7PeeRRRF59G/QJjn/Jp08Ygcgxt2LuvEc7HC9Yswb65PEAgAt71+GOEVo8ueMCplypQdPeda1tUyYgv2CN3Xu2DwCdXdNRAJiTk4PsqXfg5Lr5qP34jY5lAj5+AyfXzUf21DuQk5Nj956f7t2H8CvG4tS7Cx2XGHh3IcKvSMdnn3smeVIgpN4PFsxOSdSLuZO1MSlvK1oUZs20GgHDolvsjvsjayMzRaoLs1P2DMdIIudcTfnvatZGjVaLwfM2oeV8PWpXzUT2VRIbvzTjjqt0yD+kQb97VkITFoljSyahpaVjOQF3Mzfm5+dj7rzHUFNTDWkxQ4To0K9ffyx96QWHAVz7awJwWmIAgMezRXo69X6w6Mn46Lg6LhH1Cu5kbezyFzxOzvsjayMzRRIRBSdXa7+5mrXRlrjD9MUm3DFCi41fNmHHneEY/855TLmyDwr3roP+6l86TNzRUFeDaDcyN+bk5DgN1pyxXVNotOj38/vQ7+f32b1Gtlg8ni0yKSkJK5Yt7RW14AIVl1MS9WLuZG2MCbPPctVedF/H5wdG9VFsNzBa+bwrmCmSiIh6wtWsjdnTp6OptBBNFdsRKiyYMToUY+K1uHOUDqHCgqbK7Wj6YovDxB0RUTHdumZ4VEzPb8gJZovsvRjEEfVi7mRt7KtXnqgPD3V8PnWw8uAzZnCs4nlXMFMkEQUDo9GIm39+vWLKfE9xpQ6aJ9r6SvJVyR0yPloaa1C/4Qm0NNZePOYoa+Mjcx9Cc8XfMflKgY1fmvHYta1B3mPXhmLjIQvu+DHQXLnNYeKOoUOHdSvL5LBhQ925tQ6YLbL3YhBHAcOVmmT+uqY/+uqKp7OSERehd3guLlKPP9x2ldO2TeYWp+eUzpcdrVNsV/p9reJ5VzBTJBEFgxcWLcTnn+7xWgFrm/Z10MInL8bgeZsQPnlxl3XQ3G3rS68seRFnv7iU8fHC3nWQxi8vJicxVR3C2dIPsfSlFzu069u3LzSyBXqNFTNGh3YsLj5KB73GCi2sCA8Pt7vmkSOH0XhAOXNj48FiHD58xGP3yWyRvVeXiU2EEG8AuA3AKSllStuxpwHcB+B028vypJRb2849CeBeAC0AHpJS/r2rTnDTNtnS0nfOamj7Idwb2f5cvaY/+uoqq1Xit6v34eN/n0b7SgFaAfzsx3H4651jnfb1tmW7UHG8wel7pwyKwgcPjbM7nvZcMaobmx20aDUgIhT750/o/k10k9UqUXjwOFbuOgxjfRPio8Mwc1wiskYNCpjPg1r1psQmHCPJV4xGI5J/nIQd0zQYv86Kyn9/i4EDB3r8OgaDAalpYxGRlecwi6Kp6hAaCxehrGSfXYIKd9r6w7PPPounn1uE8OQb0XJoO/bMCEXGajM0V47HucodeHp+Hp566qkObe6ZkYOWsg0o/I8ZlQ9EdMiKaTxrRcqr55D1oxBoU6dg1Vv5HdpqNFoM+PV8VG9diojRNyFi1C8QEhUHS8NpNB78OxoPbEP/zDmo3rzILimKO4qKijBlWjb0yROgT5lw8ZqmimKYKouxYW0BMjMdJzkj3+rJ+Nidmbi3ANzs4PjLUsrUti/b4HQVgKkAktva/EUI4XiNE1E77qSl9/U1/dFXV205cBz/+rYGnUq9oUUCeww1in2N7mJPXEzfUIfH+zpZ1mjjbNmjuzQagdtTE1A4OwP7509A4ewM3J6awACOvO0tcIwkH7Cl0h8Tr1UsYO0uV9Pgu9vWH5566ils/2grIo/vw10jgTHxWsxIkYg0fo7tH221C+AAYGfxNqyrNGNqis5hcfHfJIdgbYUZO4q32bXVhfWFrv9gDLj9cVw4UgbjGw/i+z/9CsY3HsSFI2UYcPvj0A0YAl0fz+7jzszMRFnJPkxNi8e5jXk4tmQSzm3Mw9S0eAEd1EMAACAASURBVJSV7GMAp1JdBnFSyn8C6G7KmtsBrJNSmqSUhwF8A+AnbvSPgsSq3YftgiKbJnMLVu46HDDX9EdfXeVOX/d/p/xtv+9ItcPjgbWglMi7OEaSK3q6t61zQWtnBaw9wVYHzVxrRM2O13F0eQ6+e2Eiji7PQc2O12GuNTqtg9a+hpozztr6y4gRI3Ch6RwWXNe69WDBdXqYms7hqqscbzeoaWqBNkR38fWdLbhOjxBdKGrOO87+XPvJWzjz/v+iz7AxiL9nBYY8uhnx96xAn2FjcOb9/0XtJ295ZRy1ZYusqz6FlhYL6qpPYcWypQExI0qucWdP3INCiINCiDeEELZMBQkAjrZ7zbG2Y3aEEL8VQuwXQuw/ffq0o5eQSrmyX0xNaenVlM7enb42W5SHEWfnz5vMiu3OdXGeqJfgGElO9XRvW+eC1s4KWHtCQ10NzNVHceKdeRAhoRiY8yKG/H4TBua8CBESihPvzIP5zPcOU9I31NV0K22/p9PZu6Onz7b5bA3uSrWfhbOJj9RgxugQNJ+1v8fm8424YNiPyyYtQOz1M6CLjYfQaKGLjUfs9TNw2aQFuGDYj+bz5zx3g9RruRrEvQogCUAqACOAl9qOO1q35PAnPSnlX6WU6VLK9Li4OBe7QYHGtl8sb1M5yqvqcaaxGeVV9XjyvXLcn1/iNJDzR4r4LlPhOzmvpnT27vTV1fIEffXKyzDDuzhP1AtwjCSnbLNqO3L7dGs2rfMsnI23ZuPCI6NR/eHLioFG9dal6BsZbddWbensXXm2el0IXt17HuKZBqdfr+49D72DrQOhfSMQmXab4nLTyKtvQ2hf+6QoRJ25FMRJKU9KKVuklFYAr+PScpBjANrnLP8BgMDZIERe5+p+MX+kiO8yFf4Qx6nw1ZTO3p2+ulqegDvQKNhxjCQlLyxaiDtHajAmXovcFE2Xs2mdZ4psejob190lnMOGJSJitPK+tohRE5CYOMzuXOd09o7S9iuls7eVJoiIjkV4qEBEdKxXSxO48mxzZj6AqIQr8MBP+0L+Icru64Gf9kVUQhJy75vl8JoRox1toW13PvVmp+OoGko3kO+4FMQJIeLb/e+vAFS0/XkLgKlCCL0QIhHAcACfu9dFUhNX92BNHD0I117RHyGdklCEaAQyrlBOEe9quv8uU+EfdZwK352+AkBzcwuyX/8MiU98iGFPfIjEJz5E9uufodnJc3PHxNGDkHHFAIRqO36rh2o1Xabed7U8wXkXSxPYqKV8A5EzHCPJGdvMz+PXtI4fj18jFGfTnM0U2fRkNq67Szi/++5INwKNTBw58p3d8UfmPoRzB4oU0/afO/iRw3T27UsTaPr0hU4DaPr09VppAlefbc70qTCfNGD+tY5/hJ5/rQbmU98iZ/pUu3PmpvPdWm5qvmC/1UEtpRvId7oM4oQQawF8CuDHQohjQoh7AbwghCgXQhwE8DMADwOAlLISwAYAXwL4CMAsKaXnfzKlgHXcxT1YVqtE2fd1sHT6Qd1ilSg9Wuv0B3hXl28CwImGC4p9PVHv+LyrfQVaA7iUZ7Zhj6H64hoqCWCPoRopz2zzSiDXegXZ6YhEV+VFQkI0+PSJG5F7zVD00WkgAPTRaZB7zVB8+viNCAlx/M/HIDeWcLrzeRL5A8dI6gnbLFz7/VdKs3HOZopsujsb15MlnI0Ndd0KNM41OPlFqNWKUxufQXXxqzhfXoyP7wxDU0Uxqotfw6mNzwBW+4QfBoMBU6ZlIyIrD7rh18JScxwfzwiHpfY4QodnICIrD1OmZXt0xqnzszWeteLm/HM40djaP2fPdsOad3Df2DDFz+S+9DBsWPOO3bmoWNeWm7Z/PhEZuR2WuEZk5Hrl+VDgC+nqBVLKaQ4Or1J4/R8B/NGdTpF69dVp4ThnYStnS/ueLqzEmXOOa4udaWzGMx98iYW/TLE7153lm7enOswbgPjoMJxRqGfmLNhwta8AcPfb+9Dc4jhjVXOLFfes3o/8mU5+LegC2/Np7lRjwNwiu3w+QGva/rShsSg7Wnex9lra0FjFtP33ZiTiyffKHc64dbWE053Pk8gfOEZSd9kCqcr7Oo6Dj18jkLLyTTyWt8Cu7tu+vZ9iz75GLN2j/N7XNv9L8fyl8gQa3DlS4IVFC7Fk2Z8dvta2r00XG+/wPOB8X9uSpcsQPuZW9Esej4Ytf8RdI0Vb2n4z3jlWgYF3vgxTRTFefmU5Vixb2qGdrTRB7dsP4J4xOoyJ1+KeVB3e+ehPiJ3xZ5jbShO0b+eOzs82VAuEhQBDlzai/RDU+dm2tjuPZZ8qv/+1FvvPJHv6dKwv3Q5dRq7Tdo6Wm3andIOnnw8FPneyUxLZcXWeZOMXxxTP/63kqMPj7qTQd3W/mKt9BYB/GZRCXGDPN2cUz/fUyl3fosnsOGi8YLHi9X9+67Stq7NiE0cPwrjhA+yera0YutISTjWVbyAi6onOs3A2SrNxuz8vhZQSpaWlCAsBjj8SAfmHKBx/JAJ9dcCBAwcgpcTuz0udXren5Qk672tzxNm+NluJAaHTw1p3HAuua60nuuC6UFjrjdDo+jgsMWBrZzr5Lcxnvsf/jGtdyv8/4/QwV3+H5pOHPV6awPZsjx8/jujwPuirAz6eEY5wHRAT0QdGo9Hhs3177UZERMdiYM6LGPr4B3ZfA3NeRER0LN5eu9Humo/MfQimim0Xl5t23jNoqjoEU2Wx3XJTNZZuIO9jEEce1dRsUTx/3sl5k5NAo6vz7qTQdzXYcLWvQNdBrqcXCx6pVk5TrHTe1SQ1Go3AX6ZfjclpCR2WYU5OS8Bfpl+tOIunpvINRETd1XkvXGdd7Y277+4c3DtG12EZ5j2pOsyckd3ltXuaQr9zoNGZs0ADuFRi4MLedbhrdEiHa941SoumvesclhiwtTv/0Z9wj4P7PPfRn7xWmuCFRQsxPKYFd40ObZ01HB2K4dEtTp+POwXNk5KSsGFtARoLF6Fx12qc/+cbkMYvce6fq9C4azUaCxdhw9oCu9ptaizdQN7HII48alBMX5fOu5rO3p0U+hqNwGs5aXh+0kiMTIjGgIhQjEyIxvOTRuK1nDSnwYarfQW6ztzo6cyOnfft9eS8q7NiVqvEA2u+wLtfVOGC2QoJ4ILZio0lVXhgzReK+9rUVL6BiJzraTHr3s7ZLJyN0mxcWVkZKisqkTeuY6KpvHH6/9/emYdHWWT7/1u9ZSWEzSEEZYkLYxYSEsQxCd6fjqPooDOPzriwqWyKMoLz/BBZrnqZ6/jj3quCOjojCozrjIwLqDgickcICASSAMGN4EbSsgSB7OlO1++Pzhu6336rulNvJ51On8/z9AN5q09XvdWVVJ8+db4HlZUHsG/fPmG/vlE435wvWTRO72g0V3+G0397AM3Vn0sdDcB7FLPF+SWaDnyEJYUWvz6XFFrQVPkRWn44FHAUMyW1PxqrSjuicL52i4vj4DrxLRq/3htSaYLy8nKkD0qVzovv/KxevRqHal1YUOiNGi4odOBQrQurV79oOD9mo2ITJkxA+Z7dmDgqBc2f/Qtbpiag+fN/YeKoFJTv2Y0JEyYE2ERb6QaieyAnTgFSzxMzvWgE4qwC58fKhEcUVeXspxeNQLxAYCPeZglJ7l+v7xFE70N5rABw/jnJUtsLfiJud7s9WPLWfoxauhEjFr6HUUs3Yslb++F2iyN/Nov8V9wuaVeNiqlG8IDoKt9AEISYzhaz7u3s3rkDK7Y3SGuLrdjegF2fBuZR6aNwGqFE43yjcMtLWrGrug3LS1qDRuM0R+OW/DQ0vrkU3FmJxjeX4Jb8NKGjAXiPYjZt+XNHFE7f5+05VjRteTbgKOak225D0z//pyMKp7e7M8+Opg/+W1iaQD9fDXWnQ45SalE43+ifLBoXjqhYRkYGUhPtmJ4fh7w0K6aPiUO/JIehYwyYO+JK9F7IieskpJ4n59rMweCCeBIHw4SLBxu2qcrZ/zI7DX3ijYtH90mw47oscWK26nv579ddHCDZr+GwWrD0WuMjFgCANpe4DQB3G7e73R787LGP8fLO7/yiWy/v/A4/+38fCx254QPkkdFhA8XtqlGxVVsPSyN4z28V5+GZyacjCKJn0Nli1rHAG++8j359ElBzfzLKZiUiwQbsnZXkzWu7KxE19yejf0oC1q33l4kXReE0ZNE4fRRubUUrNk9NwtoKV9BoHOB1NBY/+ABsVoYt05JgtzEsWbRQ6GgA/vL7Rn2K5PdHZ2fC09LYEYXT2y0ujoOnpRGjszOl86zN15ZpSSFFKfVROA1ZNC4cUTGn04k1a1ZjUaFXX3BRoQ1r1ojfCzNHXIneCzlxncRMlCEW+I/3DkrVF5e9b/wHSFXO/t39TjQI8uzqm91474BTOFbV9/L9yh9gFRy1tFoYNh4Uf2A5VNsibJO1P7yhEsfrjduO17XgkXcPGrbNKB4pjWzNLB4pHItqVOyb2kbhawLAtyfE7apHXAmC6DmcVUK0dqoYdW/GNyI2c0MzpvuoL85Y3yyMjImicBqyaJw+Cjd1tLfPKTm2kKJxvq8R6nvpK7+/vKQV09rzzKbm2Dv6NJLf/4+lD/pF4Yzs7syz45GlD0r71+arY26DRCn1UTgNWTQuHFGx5Y8uw+RM+EX/Jl3MhfOrP+Lq+tEJ3uaG60dn0COuRO+FBasV1R0UFBTw0tLSSA8jJCY+tQ37q08L27PT+2LD3KJuHFHPYtTSjWiWCHvE2y34fJnxMQwVzLwfqrZm+hy+8D2hncY3j10XcE11XrVo49avTvhJ/muRLZljpGrb3WuAiD4YY3s45wWRHke0EE17pNPpROZFGaic6XUenHUeZK1qQ+UXhwPk82OJokvyULK7HIBXxr7qd8kd85Oxsh5N7d9FFo7N9VND/ElqIo6FIOh0Tt8EHD3l/wWZb5/xNuBwiH1qqLyXqn2mxFtQ18KD2vWJYzjTbLy/lJeX47KxeX5ze/5T9dhRWoGcnJyA51+Sm4X9lZUdfQXcf3vfWVmZ2FV2oON6VVUVcvPHInniIkNxk5bqz1C/4VGU79lt6FQ5nU5cfOFIHJxl8+vXWedB5vNtOPileH6rqqrwxIqn8PIrr6Lu1En0Se2PyZNuw/z75pID10vozP5IkbhOQup5cswoN6qgWlw8WJusPRJrQHVezypFDtUpRQ4NqhSpGhWzBYmWBWsnCCJ66awSYqygydkX5GYGqExOz7NjbG6WoZz90VON4JwHfegdON8+5917N2ZfkuTX56xLkjB/7hxpeQKV99K3z7vGJfvZzh6XLOyz3sUwIH8CUhwIVOHMsyMlDhiQPwENLvHH1s4qeBYWj/cbox5tzEXFl/tdNxsV00fhfPuTReO0vp9e+SRO1R5DW5sbp2qP4emVT5IDF6OQE9dJSD1PjhnlRhUSBcf9NETHAQH19zISa0B1Xs8qRR7RKUUeCaoUCXgduRty07FhbhFKl1yFDXOLcENuutT5Gz4gSfqawdoJgohO9PXINILlXsUKZlQmVRGVNghW0sDMe6lim5ySijPlH8DlQUeNOI3FxXFwtQF1Ff9EUkqqYZ8qc7t75w48WVIvFZt5sqTeUGzGV/ilYd0iHHn8RjSsWxRU+EWfC6cnWG4cQfhCTlwnIfU8OWaUG1UwcxhY9b00swZ+kmKcnK4xWNCuOq+RyOGcUTxSqhg6c7w4D48giOhFH7nRoGicF99Ika+EfmdqvnW2dINvaQN9n6KSBpqd6nvpa6vvU2R7wfB0JFh5R15cwPzk2RFv8eDC4emGfc68Y7LQ9g7B3GpRQy1yOGl0AoYkM0zKTeiIGMoilSpRMVEUTiOUaBxBaJAT10lIPU/OwxMzMTDJYdg2MNkhVJkE1Eo3qBYXB7zvZWHGAOiDShYGFJ4/QPheXj96CIrOHwC7rpSC3cpQJLEDgC3zLhe2AcDHgnbvvBqrcA5MsgvnVbXWmxm0+dFXmrAyBJ0fgiCiE1H0RSPWo3H6SJGvhD4QejTuocULUbJtKx5aLBf4AAKjcPo+RdE4M++l3lbfp8j2ZG2tXxROb7e4OA4uD/DjydqAPrW5ldkekMytNubKH1rQ4OI46GzpsrW6vWQrnt7ZLI3+Pb2zGSXbPgl730Tvg5y4TkLqeXIsFobc81ID8p5sFoa8c/tJRTRmv1SKBesq/OT+/++6Csx+qVToyA1OiZeOZ3Bfcbvb7cG/vjwO/Ut7OPCvL45L668BDExXSoGBgTH5+x8fb8MVFw0KKMLAAFx50SDExxsfsfB4OE43GzukZ5rdwvmp/rFBOp5g7Sp4PBwVR86gTTekNg5UVJ+J+TIcBNEbEUVuNGI9GqePwukl9EOJxjmdTrz+2iuwWzhef+2loE6GPgpn1KdRNM7Me6mPwhn1aWR75sdav0ia4fzk2XHq5ImAPm/7za+D2+bacetNNwjn6dqRwBe1HmyZloQvTnowYSS6ZK1mZo/G3QUO8IdSUHN/MvrFe0tN9E9gcP4+GfyhFNxd4EBWzuiw9030Pow/MRJStDyhG3KNw/qxzPqKGmyvqoVb90Hd7eHYdsh7fM9o3t4ur8bmz48FOFSuNo7Nnx/DOxXV+HVe4JHC1ETjqJ9Gv0Tx8cU71u6GS+BQuDwcd/61FC/PCPwqcn1FDUqqTgSUUmht83QcURStjfUVNfj065MBx0A5gO2HTwpt71i7Gy69V9TRr3isENTsO9sc/i8dQimHsOxXWWHvlyCIyLF75w6U7K7HkyXy5xW2BuYXxQLffX0YpaddeHq3Cw4rOmTwb860YdiT9dAOTJzTt0r4Gg8tXgjuacPm25Nw+ZoGPLT4Qfz5hdXC53vfkwas2A5pn4Wu7QZ2au+lr620T52t29WCZ3YDzwSZH6OMg+oj3+GZQ57gtvHfBdhqUbhhiS1+pQlKvmvBxrWrsWDR0rCqqn686UN862zFs6Wt0rEOq/kwbH0SvReKxBFhRfX43uObvgxw4DQ8HHj8wy8N2/Z896N0PKXfnhS2ba8KPJbhS8mhwG/8AHNHFFVtVcda1yI/bloniO6ZYd3eI9L2N/Z8H/Y+CYKILL75RbKHKL+ot6OpTNbU1CApMQFLx3u9kaXj45CclACn0ylUmQTORuE6HI08e9BonFZgvGxWIpLs8O/TwVA+O9GwwLj2Xn744YdIsAEzx9jRLx6YNcaOBBuwefNm4Xup2Qa7T73t6WYekt3p5sAPCnVtdgyZ9TzS7/krbHaHn63NEYeh97yEIbOeR11bYEqCbxROO+q6qDiuy6Jxp1p4SGM91RzWboleCjlxRFhRl+2X/8WqEbSbKWkQ7FCfqN1MiQFVW9WxugXRu1DbVejuMhMEQRDRgmoJBi0K90Ch90P/A4Vx4G1t0tw4ra+1FW5M8ylondbHgqk5dqytcEv7vnPyLbg504Z1B13YPDUJ6z5z4+YsG6bd9psuu08VO+5qha3vOWje+TpuH23zs709x4qmna/DljII3NXqZ+ebC2dUmqArcuPOnDoZ0ljrTom/gCYIDXLiiLDS3fL73V3SADB3j6q2wQ49itojMT+R6JMgCKKnoyrb7xuF09dPk0XjNAn95/a0YkGhf+rBgkIHnittFUrob9q0CbUnT8JuZZg22oG8NCum5tjhsDDU1p7Exx9/3CX3qWLH7A60OL9E04GPsKTQf39ZUmhBU+VHaPnhEJjdfw6MonAaXRWNS0ntH9JY+6T2D1ufRO+FPk0RYUVVfr9vgjw9s2+CsTKjmZIGSQ55jbnkOON2MyUGVG0vyxggHWvh+QMNr+ef109qVzAs/BtFd5eZIAiCiAZUZfv1UTiNYNG4bbvKUJCbiRk+zp9vn1qRcaNjkb5ROM0BXFDoCCkap3qfqnaZF2eiYfOzfpEtX9vbc6xo2PwnZGVm+rXt3rkDb1Y2dYii6O3uyLXjzQNNhk6uKpNuuw1NW/4sHWvTlmcxedJtYeszXHS2vAXR9ZATR4SV60cPQeH5AwzVKYvOF5dgSLTLnThRUe+HJ2ZiULKxuMmgICUN4oMUCo+zGbd7JfQHwmH1//VxWC1By0yo2q6eNjagpMFZW4YXpxYYtp1qbDW8rvFjo7EAiRm874mxoMygPnHS9yQSqJS2IAiC6Ayqsv1GUTiNYNE4UQFsDVFZA30UTn8MUxaNM3OfqmUN/n3xQriPHw6IbGksKbSg7fhh/LvO2X36L15RGH1xcY3FxXFgDPjTqrXGg1Jg8m23wHW0SjpW17HDmHzbLWHrM1wsf3QZdu0oiVmF2Z4IOXFEWPF4OMq/O2WoTln2/Y/CD8aNLrnARpOg3WJhGD00FVadyqKVMeSemyot+ZAmKT8AAGmpsnYOfSYahzcxOzidt7VYGPoKyg+kxNuE93m0Tu6kHT0TfifOZrNgx8IrMOXSYYi3W8AAxNstmHLpMOx44ArYBIXAI4HHw3HXy3uw6K39fqUtHnxzP+56eQ85cgRBhAVV2X5RFE5DFo3zLWsg6tOorIFRFE4jWDRO9T7NlDXY/skWzMhPkNpOz0/A9k+2+F1XnR8z/P3VlzBzrHysMwsS8PdXXwpbn+FAc7I3T4mP6XqPPQ0qMUCElYc3VOJEg3H050R9q1BePslhw8kGl/B1Ex3GS3V9RQ22H65Fm84BauMc2w7VSuX+c89NxYGaM8I+8841Poq4vqIG2w6dQKtOFMTVxkMqMVBSVWtoKxuvd16N5+dEg0s4r2l9E3CiXhyNC3eOoobNZsGyX2X1+FIC2nupVwxtcrUFfS8JgiBCRVW2/8MP3sOUHLmjMTnHjvc3bgho8y1rIENf1qDuzCm8WusRRv9uzrThhTIXEuynAl5L9T7NlzVowtM75cJhhR5/W9X5MYN3rI1YuUP+vEJ3zyrFoTnZeWkWTM1mWP7oMjy+8plIDyvm6TlfiRO9AlV5edV4hxm5//LvAzcgX8oE5QtWbT2MJoHCYrPbg+c/OSx8TdXxqs6rmfy9WMDM+iEIgggVTXq/rKwMCTag5v7kjoLPiXagoqLCUHp/6NBz8WypC+yRMx2PhP884/fzs6UuDB0amGvsW9ZAKzVwdYa1o7SAqKzBZ4eP+Mn869Fk/z//ulp4nzU1NeibFI/U9mLW/eKB1OR4YYkBX7t+fRL85sd3rLKyBp2d26OnGjHv3rsxr9D7fP1jXmEy5s+dIyz7oIK+FEdNTQ2uvmJ8x/31xFIc+qOuwYRmiO6DnDgirKjKyze1yo9TNgrazcj9/3BGXtZA1P5NbYPUTtauOl7Veb1+9BAUXzAwwJFLsFuD5u+ZIVryzMysH4IgiM6iP8IX7MieVutNc0zmjXMgwQbMv9Th5+Toa7354ltqYFd1W9DSAmaONvq+xgWpbbi9Xdly2mgHLujb1iUlBjQ6O7dm8vDCRTTkmZl5T4iuhZw4IqyoysurSu8PTpHntcnaVfvU5/t1pj3oeAV5eqrzarEwPDc5H4/dmI3s9L4YmOxAdnpfPHZjNp6bnC/NGVQlmvLMurskBkHECrGiZNeZ+xQJjYgERgDvB+ip2Rak9bHAWefB2opWbJ6ahLUVLvxQ70FaHwumZFmEH6g1R2Vapr/t7VkeoYOilSbwjfbpH6LSBFqfq1evxqFaf2XLQ7UurF79orTEwJo1qw1LDKyR2AHqc2vWWTVDd+eZVVVV4Z659yF1wCBYrFakDhiEe+beh6oq8XFR1bIPRPdATpwC0RJlMIPqPd4YRF5eJD+fe26q1C5PIJWvageoHzW0WeS/NnZJe9DxCvLwzMr26zVTQtJfUSSUPLOuQGXN0nFTgugaoiHCEA46c58iIQ1ZxGj3zh1Ysb0B7JEzGL6iHrdk2ZGXZsXNmTYMe9LraK3Y3iB0qPQFv7V6b7Jo3LZdZSEdMxQd+fONwvlGb4JF4+65ayZuHeUxnJ9bRnlwz10zDO0A9bk146ya5WyembXLI1sbN25Ebv5Y/K3sKJJu+iPO/f1bSLrpj/hb2VHk5o/Fxo3GkVzVsg9E9xDUiWOMvcgYO8YYO+Bz7b8YY58zxvYxxt5ijKW2Xx/OGGtijJW3P57rysFHgmiKMqhi5h7zzgvipAwzblfNTytTtAOAazMHo01wL20ejgkXDzZsGz4gUdrnMEn7nm9qpbal35wwvL5kwk+FBb0ZgMXXjDJsi8R6jUSemep9Ruq4KdF7oD0ykFhRsuvMfarK/WvHKctmJSLJjo48taXj45DsYB35bUbHKfVRON+omCwaZ+aYoVEUrsNOEo3bsWMHPnj/fSwdb1wyaOl4Bza+/z4+/fTTgDbVufXNT9Ny8fbOSvLLweuq/LTuzDOrqqrCb2+dhOSJi5BcNAX2fmlgFivs/dKQXDQFyRMX4be3TgqIyPWE46aEnFAicWsAXKO7tglAFuc8B8CXAHy1bas457ntj7vCM8yeQ6SiDN2JmXtcu/1b6WuvKTFu/6ZWnjj8raC96li91O7QcXH7f7x3EK1txrlkrW0eLHv/M8M2M9G/r47L7/PQMeP2P2z8TCj+wgH85wefG7ZFYr1GIs9M9T4jcdyU6HWsAe2RfnRnhCGSdOY+VeXs9ZE0fc02WURN1dbMMUOjKJyvnSgad++c2bg9L9DG1/b2XAfuvXt2QFs4SgV095rtzjyzx59cibisXyAu/aeG7XHpP0Vc5lV4YsVT0jHqoWhc5AlaYoBz/gljbLju2oc+P34K4KbwDqvnEkqUIdolyc3co+oHeLdHLtzhErS3uNUEP4DQFB+NJPJVo4ZA8Hw6l6BddayRWK+RKGtg5j4tFoYbctOj/veWiAy0R/qjfXtfOdMb3V4wDshatRoLFi3F4MHGpxuikc7ep6qcvSa9H28DDv8u2a9tQaEDGSvr0eRulUjvd97WjNx/ydZPcOCoC+tvTjawONsvtv7L7/qhzz/D3mY3nt0tr12aEn8w4JrZUgHdvWb1/Wl0Vb+v0HgxnQAAIABJREFUvPoqkm76o/Q5cVlX4eVXFuHplU92XDOzDojuIRw5cXcC8I3jj2CMlTHG/sUYKxYZMcZmMcZKGWOlx48fD8MwuodYULMzc4+qQhG2IBEPUXuww4CydlXFR6eiqqUZlMcagfUaiTyzWPi9JKKWmNojY0XJrrP3efRUY4B8vvbwldHXy9lr+Wkz8uMNI1vT8+OF+Wma7Z0GEa60PhbckecwtNWOGRbkZuLesXa/sd471o6xuVnCY4aFxeNx17hkafRm9rhkFBVf7ne9rtWDAfkTkN6HGfaZnsIwIH8C6lsDd3WtlEKw8YpKBXT3mu3uPLMzp07C1vcc6XNsKYNQd+qk3zV9OQTRoyeVQ4g1TDlxjLHFANwAXmm/5ARwHuc8D8D9AF5ljKUY2XLO/8I5L+CcFwwaNMjMMLqVWFCzM3OPqh/g+ybYpX2mCtrjbHLnT9auqviYKLg/DdH9AxDmtQVr727VTzNEIs8sFn4viegj1vbIWFGyU71PlQ/vmmLjokLjg1OLCm1Ys0acn7Zm9YtYUmycZ7ak2CFUfVRRewTUxUKSU1JRv+9DnGzihn3+2MjRsH8TklIEefWK4+3uNRuJPLOU1P5wnz4mfY77zHH0Se0ftj6J7kHZiWOMTQPwSwCTOPdq3XHOWzjnte3/3wOgCsCF4RhoTyEW1OzM3KPqB3gLk7s3TNAeTJXxtwXnCdtUFR/NSIFcljFA2l54/kDD6/mSPDsAKBhm/Mc3Eus1EnlmsfB7SUQXsbhHxoqSnaozpvLhffmjyzA5E9LI1qSLuTA/7baLudT21p96DG1988ycdR5c83JDR0kDWX6ZFv2777IkQ2XL+y5LMoz+XTA8HQPjPeI+8+wYENeGC4cbH3tXUafU5qg712wk8swm3XYbWio/kj6n5cAmTJ50W9j6JLoHxkPQGm8/7/8u5zyr/edrADwO4HLO+XGf5w0CcJJz3sYYGwlgK4BszvnJwFc9S0FBAS8tLVW+ie5EU8Hb+tUJNLnO5uBoTkpvEEMwe49utwePvHsQb+z5Hi0uD+LsFvwm/1w89MuLYbMZ/+HK/8Mm1EryqAYmO1C65CrDvi7942acaAi0HZjswKcLrxT2qWqbv+xD1DaIz94PSLJjz9JfGLa1trYh8+F/Gua+2S0MlQ9fDYcj0Bm5bsUnqHTWCfvMTOuD9+4bH3A9FtYrEDv32VtgjO3hnBdEehzhgvZIr5OSeVEGKmcaf0B11nmQtaoNlV8cjurcONX7nD93DlD2Ep74ufi78/kfecDGTMXjK5/puDZuTDZ2lR0Q2mhckpeFnXv3+13Lzx6FvQe+CGo7JnsU9uw7K+RVXl6Oy8bmoep33mORszY04Y2DLtycacdzv0yAs86D85+qx47SCuTk5Pjfv+L8XHzhSHzz9dcdfc7/oBlrK1pxe64Dj18dD2edBxkr6zFi5EhUfuGf26Yfr1GfRuONxJotuiQPJbvLgz6vcGxu2I4pVlVVITd/LJInLjIUN2mp/gz1Gx5F+Z7dyMjICEufhDqd2R9DKTHwGoAdAC5ijB1hjE0H8DSAPgA26WSSxwPYxxirALAOwF3BNqdoIxbU7Mzco8fDMefVvfjH3iNodnnAATS7PFi35wjmvLpXKPWepli022JhyD0vNSBnzmZhyDu3n3SsFgtD7rmp0D/FwrwKlCLbIanyEgOydpvNgssvHGTY5+UXDRI6nEfr5MneovZYWK9A7Nwn0fOgPdJLrCjZqd6n6jHDywqLMXNsIvrFwziXLoFh5thEFBYFfolnsdk6csRq7k9Gv3h4JfQTGJy/T+7IGbNa/Y9q6qNwrx1w4eOpSXh1vytoNM63OLlofoyKkyclJeJOnz6NiprfmWdHUmLg/mpW+bM712wk8swyMjLw99deQf2GR1G/9a9w/egEb3PD9aMT9Vv/ivoNj+Lvr71CDlwUEoo65a0Gl18QPPcfAP5hdlA9nVhQs1O9x1Ck3o1eM2hOXKLxmf71FTXYXlUboPro9nBsOyTuDwDeLq/Gli+OQ+9Xejiw5fPjeKe8Gr82OHI5vWgEHnxzv1/ERyPY0b31FTXYfrjWsM+SQ7XC8ZpRfIyF9QrEzn0SPQvaI71Es5Kd0+nEHZNvwZpX/hY04qJ6n74fyn2jckbRN31/u/c2Co8K3pxpwwt7GzEWgfPqq9rosALT8/wLhWvbtF61UWQ3OccutTs7Pw1Y4TOceBvQ7NbNj8t/vFqfz3RyrPrxyhApf0bjmu0sEyZMQPme3XhixVN4+ZVFqDt1En1S+2PypNsw/68UgYtWwqFOSfRgPB6Ot8uqMfGpbSj4wyZMfGob3i6r7rKi5KqFnku/lX8ZvVtQJNtMYeknNn2JNsFx4jbO8fimLw3brh89BEXnD4Dd6h/dsVsZis4fIBXuUB0v5XwRBNGTiWYlu+WPLsOuHSUhRVzM3mdnizy/8c77SEpM6CjyrWfp+DgkJxkX+9ZUG2tqavxeQ7PRClrrVRt97Rx2G2aNseOalxswO9+OOLtNaKeN11eBc944BxJswPxLHX5KnPrxqo5Vs513792YV5hsmIc3rzAZ8+fOMVT+jNY1q0JGRgaeXvkkTtUeQ1ubG6dqj+HplU+SAxfFkBPXi9HyhBa9tR/7q0/jRH0r9lefxoNv7sddL+/pEkdOVeq91S0fi6jdjLR8zWl5KQCntJ2B6bQkGZhQgCWU8cjaI6H4SBAE0dvRnKrNU+K7RUGzs3L24Tjypyqh/9CSBzEli2FthRu7qtuwtsKNyVkMDy95UGjj25foWGRn7jeUsUZC8ZEgegLkxPViQjnaGG6U68RZ5c6PqN2MtHwwUR+PoH19RQ1Kqk6gtc2/NltrmyfovKqOl3K+CIIgwo/mNOSlWbs8Z09Fzl41l85Mn5rda6++gllj7H6O2Ox8O1595WWhne94h6+oxy1Z/sciZeMNd+kGjd6Sj0kQesiJ68WYOWqoyh2Fw2EVRKOsjOHOwuGGbX3i5OmZfeKN280cMxTVnutoTzRuNzOvqvMDnM352jC3CKVLrsKGuUW4ITedHDiCIAgFOnu0UfQa11x5eUg2+iiVr4S+yMnYtqvMsIi1vpi16Mifb5/lTjfS/6cO+466gzo2vlG4KTntOXHZtqDROO2IYrBjkUbjVZX7N+voEkS0Qk5cL8bMUUMzuXSiCJaHc0m16yCvK3jN60cPQWFGf+gDdVYGFGb0lx4zTAziOCY5jNvNzCsAcMG9csjmhyAIgggnqscM9a8RSj6d3mFcXtKKXdVtWF7iFawSOZCiItYasmLW+j5nbmhGg4tjxvpmaZ/6KNzCIm/fC4viQorGafPSmbk1cyQy1nLbCEKDnLhejOrRPTO5dC9u+1rojnEALwiiVGbGWnHkDNp0nbZxoKL6jHSsTYJomoYo2iYqd9DR3lfcvrrkmwBlSg0PB17c9o30tQmCIAjzqB7dM3qNUPLpVHPFVOXz9X2WO92oPO7BlmlJqDzukUbj9FE4X0cslGicytzSkUiC6DxBSwwQ0YuqFL5qmQAA+Ka2QTomUbuohIBGvyTjbyEf3lCJ4/XGNdKO17XgkXcPYtmvsgzbmw3mJZT23HNTcaDmjNAu79x+wjazUTyCIAjCPPKje952key//jXy0iyYms2kNr5y9jIJfb2cvap8vr7PBBtwZ3ufd+TacemqRjS5jfvctPE9fON0Id4GHP5dsl/bwqI4ZKysR5MbGF7zrnReOjO3sST3TxDhgiJxvZhfZqchWXBkMDnehuuy0gzbzOR86eu1hdq+57sfpXaiEgTr9h6R2r2x53thW0OQSJyovSzIWPd+Jy6XYEaIhSAIgjBPONQMO5tPt21XGWpqatA3KR6JdvjliiXZgdTkeDidzoAjf+WfVaFfnwR8ODkBCTagbFYirs6wonx2IhLtwEdTEtA/JQEVnx827JNzjrIy72subj+Subg4DowBFRUVhscM04cOhcMK3JFrXJvu9lw7HFYgPT2wjqrq3Grz41uewK+oeUqC4fwQRCxDTlwv5t39TtS3GH97V9fkwnsHnIZtZqJFNot8SdkF7S0uj+H1YO2qdmb49qR8fr6rFbdTvTeCIIjIEinZ/uWPLsMFqW24fbTDz27aaAcu6NtmaKv1s2hzC6bn2f3k/u/MtePBzS1B+9UfyZQdwQTM1aYzM7eqwiYEEauQE9eLeWHb12gSODHNbo8womYmWjR8QKLUdthA43aHTa7oIWqPs8uXcLB2FdweuWPokrRTvTeCILqbzigoxgKRkO13Op1YvXo1DtW6sKDQ4adOuaDQgUO1Lqxe/WKArTbWyuMeTM+zY017Lt2a8lbMGGPHgWMe6VhFwigyQZTljy7DlCwmdcQmZzJDp0p1bsNZ643WOxErRH1OnMfDsb6iBi9s+xrO001I65uA6UUjcP3oITEvva4aUVPNpQOAGcUjsfAf+9DsDnRk4m0WzCweaWhXMKw/Sqpqha87dvgAw+v55/WT2hUM6y9si7MytOgVUXzbBY6jLci6krVr9d427KvBqq1n1+yM4hGYmCNfs7TWCYJQwVdBMVieVyygHcmbP3cOUPYSnvj5WWdl/kcesDFTpfOkkvOlReGKhnqjcPM/aO5Qp3z86nhMG+1AyZG2ANttu8owNi8Ll9q/9AqKZHvz2iZle6Ny0/Ps2Om6SHjMUCSM4huN21W236/Nm5/WiJU7JJMIoNAd6Dhu21WG+XPnwL17NZ66JjDXfe4HrbBfcqfh/ASP4IWeq0jrnYgFWLCCx91BQUEBLy0t7bSdpqKoF+HQohqxXgT5lyu3SgU4stJT8O7c4oDrbrcHP3vsY0PBkEF94rDjgStgsxn/oVW1vW7lVlTKxjokBe/+LnCs1634BJXOOqFdZlofvHffeMO2yx7dhJozrULbISkObF90VcD1oPMqGKsZaK0TvQnG2B7OeUGkxxEtqO6RgDcqkXlRBjbfasHPX/eg8ovDGDx4cJhHGH1o81I5099xcNZ5kLWqTThPIrtg9pfkZmF/ZWWHUEjmn+qxeWoSfv5SIyrnJIFzIGNlPbKyMrGr7ECHXXl5OS4bm4ftdybi//y1EQfnJHcoXGb+qR5bpiXishcbsaO0Ajk5OX5j0WyrfpcsHOv5T9UH2G7atAk3XPsLqV3Gynq8+8/NuOKKKwLm5+ILR+LgLJvQNvP5Nhz80n9+ii7JQ8nu8oDn6ykcmyvNi6P1TkQ7ndkfo/o4ZSgqirFM7rmp0naRiqJqLp1m29DqNmyrb3YLbb+tbZSOVdR+tM5YmTKU9pYg6XKtgvYZxSMRL3Bi420WzBxvHG00A611giBUOKugaKW8Ih9U869Uc74Ki8fjrnFep2h5SSumjXZ435McO5aXtCKtjwWzxyWjqPhyPzstkqZF4Xzz2rRonCi/TbU8we2Tfos7g9nl2TH1lhsN52dyJqS2ky7mAfMTrlpvtN6JWCKqj1OGoqIoksKPBcq/PyVtF6kshpJLJ5pX1fdENc8srW8CTtSLo2my/L0hfRNQq2B7/egheH+/E1u/OuF35LQr89porRME0Vm0PKPKmd4c3AXjgKxVq7Fg0dKYjk7o50WPbJ5UpfB97Xyl+xcUOpCxsh5PfNpqaKeVGDCS+19UfFbu36jEgGp5gvozp/HMbo5ngtilxJ0OuLa9ZCt2lTXj6Z3NUttLWj+RtqtA652INaI6Ekc1t+T8cEb+R1TUbmZeVW1V88ymF42QRsVk+XuqSpFaXttjN2YjO70vBiY7kJ3eF4/dmN1lxxpprRME0VlUFBRjATMKilrEaN69d2Neob8U/rzCZMyfO8cwYuRrp0XktL5mjxPbHT3ViHn33o0Z+fGGUcPp+fGYP3cOjp4KPK2i2WrjnDfOgX7xwPxLHX7j1dtm54yGwwrcXWD3uz/tcXeBt8RAds7ogD4vKywOmBf9Y15hMgqLjNMczEDrnYg1ojonbuJT27C/OvCbII3s9L7YMLfIzNCiGtX5MTOvqnl4qnlmZvL3tDwzUUStJ+WZ0VonehOUE9c5VPZI1ZyvWMBs/lW48+lkdqo5Zvr+AOM8PKN+VfP+gPDltnUWWu9EbyFmcuKo5pYc1fkxM6+qeXiqeWaqOXhAZCJqqtBaJwiiM1DNLTHbdpX5Rahq7k/G1RlWOH+f7BehEjkZvnPrWypANZ8uWO00lRwzfX/LS1px00/teHBzM24cZevIwzPqNxyRSrO5bZ2F1jsRi0R1JC6aIimRwOPhmP1SKf73y+Nw+Ujp260M/3bhIPx5SoHh/LjdHlz6x8040RCYLzYw2YFPF14pjG6pRtRUxxorESpa60RvgiJxnaOze6SZSEosoJ+f+R80Y21FK27PdeDxq+ODRsZUbFXfk3Fjsv3UKkVckpeFnXv9SwX4RsXibcBNP7Xh3a/cmHihDesOutHU/v2nPioWqWiaKrTeid5EzETioimSEjkYGFjgFSaem/X7anCy0Vjw42RDKzbsFyshOhXz8FTHWmOQB9CZ9miB1jpBEKFiJpISC+gjaWvbC2ivrXAFjaip2qq+J745ZjX3J6NfPLB3VhL6JzC/yKFRjplvHt6UvES8/YUbH09NwtufuzE5LzFo/l53R9NUofVOxCpRrU4JeD/c3pCbTsp8BqyvqEFJ1Qm0tvkrO7a2eTpk6Y3m7fFNX8IjCNB6OPD4h1/i13lDDdsT7VaIS29DeCRQdawJdhsAsYKWt713QGudIIhQUFVQjBV858dhBabneQto35xpw7An66EJARvNj6ptOFQtOztW4Kxi43XDXZiS47WdnGNHfUsr/rG2dyg30nonYpWojsQRckKRpTfCeVoeTauRtKsezlUda6PLOB/ubLvxaxIEQfRWoi2S0t1o81NTU4OkxAQsHR8HAFg6Pg7JSQlwOp3C+dFsZ94xFQ4r/GzjrMCsO28XRrfm3Xs37rssyVCx8b7Lkgzz8MyMFfBGqW4axfD2F24/27c/d+PGi1iviE7ReidiFXLiejFdJUsvO7jXJBAZ0WgUtKuO9XSTvL/TTfI6NwRBEERsoipJ73Q68fprr/gV0tYKYL/+2kv44YcfDG3Wrl2DBy413kEfuJRh7drVhraqY9X6bGpuxZQc/7FOzrGjuaVV2idBED0bcuJ6MbJC17L2IX3jg9iJ24ekJkptRe2qYyUIgiCIzqI5OAvG+V9fMA5BHZuHFi8E97ThgcI4v+sPFMaBt7XhocUPBtgsf3QZpmZbpHlbU7Ishk6Z6liNonAavS0aRxCxCDlxvRhVWfr5V10Iq0BMxMoY7r/qwrD3qWoncyiB4A4pQRAEEXuoStIbReF8bUXRuN07d2DF9gawR84IHyu2N2DXp4F5W6pj3b1zB9bubfSLwvnaTs6xY+3eRsM+CYLo+YTkxDHGXmSMHWOMHfC51p8xtokx9lX7v/3arzPG2ErG2CHG2D7G2JiuGjwh5/rRQ1B8wcAA50iTpZ+YM8TQ7le56bhi1DlwWP2Xh8NqwZU/PUcqrKHap6rd/VddCJEwo4UB9/9C7HASBEGYhfbH6EMU2dKQRbhEUTgNUTTujXfeR78+Cai5P9kwJ67m/mT0T0nAuvUbwzbWN955Hw67LSAKp7F0fBzi7LaAPgmCiA5CjcStAXCN7tpCAJs55xcA2Nz+MwBMAHBB+2MWgGfND5NQQVWW3mJh+POUfPzXb3L87P7rNzlB5ezN9Kli96vcdFw56hzYrf7tdivDlaPOwQ2jScmRIIguZQ1of4wqzEjSf/jBe4aRLV/byTl2/HPjhrD0aWasDy15EFOymHysWQwPLwk8/kkQRM8n5GLfjLHhAN7lnGe1//wFgH/jnDsZY2kA/pdzfhFj7M/t/39N/zzRa6sW+yYIwFsIe8O+Gqza+jWcp5uQ1jcBM4pHYGLOEKqfRhA9kN5W7Lsr90eA9shwY6aYtaptd9sBwIghg/CN80RQ2+FDBuLr6uNBn0cQRNfTmf3RTBGtn2gbT/tGdU779XQA3/s870j7NekmRRCqUP00giB6GLQ/9mDMSM2r2na3HQCkDx0akhOXnm5c95UgiJ5NVwibGIU+AsJ9jLFZjLFSxljp8eP0DRChjsfD8XZZNSY+tQ0Ff9iEiU9tw9tl1fCIKpYTBEFEhpD2R4D2SMIYp9OJa668PKSyAPr6afPuvRv9EqyYP3cO1U8jiF6AGSfuaPsxEbT/e6z9+hEA5/o8byiAGr0x5/wvnPMCznnBoEGDTAyD6C2oOGMeD8ddL+/Borf2Y3/1aZyob8X+6tN48M39uOvlPeTIEQQRCUztjwDtkdFCZ5yqcLD80WXYtaOk02UBNIGUzVPiqTYcQfQSzDhx6wFMa///NADv+Fyf2q7CdSmA08HO+xM9j+6Obqk6Y+srarDt0Ak0trb5XW9ytWHrVyewYZ/h5yOCIIiuhPbHGEHVqVLBjCOmCaTkpVlDKmhOEETPJ9QSA68B2AHgIsbYEcbYdACPAbiKMfYVgKvafwaA9wEcBnAIwPMA5oR91ESXEonolqoz9sK2rwNsfG1Xbf067GMlCILQoP0xdunu6JaqI6YvUxBKQXOCIHo+ITlxnPNbOedpnHM753wo5/wFznkt5/xKzvkF7f+ebH8u55zfwznP4Jxnc85JUivKiER0S9UZc55ukr5usHaCIAgz0P4Yu3RndMuMI6YvUxCsSDhBENFBVwibEFFOJKJbqs5YWt8EqV2wdoIgCILoLN0d3VJ1xETFwikaRxDRDzlxRACRiG6pOmPTi0YgwW41bEuwWzGjeITpsREEQRCEL90Z3TLjiImKhVM0jiCiH3LiiAAiEd1SdcauHz0ExRcMDLBNsFtRfMFATMwZEvaxEgRBELFLd0e3VB0x0Ti7erwEQXQP5MQRAUQiuqXqjFksDM9NzsdjN2YjO70vBiY7kJ3eF4/dmI3nJufDYjEqy0QQBEEQanRndMuMIyYaZ1eOlyCI7oNxHvk6WgUFBby0lPK7ewqaOuXWr06gyXU2N05zqLrKOfJ4ODbsq8GqrV/DeboJaX0TMKN4BCbmDCFnjCB6EYyxPZzzgkiPI1qgPbLn4HQ6kXlRBipnGjtHzjoPsla1ofKLwxg8eLDp/ubPnQOUvYQnfi7+zn3+Rx6wMVPx+Mpn/K4XXZKHkt3lQfsoHJtLBb8JoofQmf3R1tWDIaIPLbrV3Q6VxcJwQ246bshN75LXJwiCIAgzhBbd8j5P71SpsHvnDpTsrseTJfLnFbZuD7hGjhlB9G7IiSMMIYeKIAiCIPwx41SpQI4YQRAiyIkjCIIgCIIIAXKqCILoKZCwCUEQBEEQBEEQRBRBThxBEARBEARBEEQUQU4cQRAEQRAEQRBEFEFOHEEQBEEQBEEQRBRBThxBEARBEARBEEQUQU4cQRAEQRAEQRBEFME455EeAxhjxwF8G4aXGgjgRBhep7dC8yOH5kcOzY8cmh85vvMzjHM+KJKDiSbCtEfS+pRD8yOH5kcOzY8cmh85Svtjj3DiwgVjrJRzXhDpcfRUaH7k0PzIofmRQ/Mjh+YnstD8y6H5kUPzI4fmRw7NjxzV+aHjlARBEARBEARBEFEEOXEEQRAEQRAEQRBRRG9z4v4S6QH0cGh+5ND8yKH5kUPzI4fmJ7LQ/Muh+ZFD8yOH5kcOzY8cpfnpVTlxBEEQBEEQBEEQvZ3eFokjCIIgCIIgCILo1ZATRxAEQRAEQRAEEUVErRPHGPuGMbafMVbOGCttv/YwY6y6/Vo5Y+zaSI8zUjDGUhlj6xhjnzPGPmOM/Ywx1p8xtokx9lX7v/0iPc5IIZgfWj8AGGMX+cxBOWPsDGNsHq0fL5L5ofXTDmNsPmOskjF2gDH2GmMsnjE2gjG2s339/I0x5oj0OHsztEfKoT1SDu2RYmiPlEN7ZHDCtUdGbU4cY+wbAAWc8xM+1x4GUM85/+9IjaunwBhbC2Ar53xV+0JIBLAIwEnO+WOMsYUA+nHOH4joQCOEYH7mgdaPH4wxK4BqAOMA3ANaP37o5ucO0PoBYywdwDYAF3POmxhjfwfwPoBrAbzJOX+dMfYcgArO+bORHGtvhvZIObRHyqE9MjRoj5RDe2Qg4dwjozYSR4hhjKUAGA/gBQDgnLdyzk8BuAHA2vanrQXwq8iMMLJI5ocI5EoAVZzzb0Hrxwjf+SHOYgOQwBizwfvhzwngCgDr2ttp/RARg/ZIObRHdgraI+XQHmlMWPbIaHbiOIAPGWN7GGOzfK7fyxjbxxh7MVZD2QBGAjgOYDVjrIwxtooxlgTgJ5xzJwC0/3tOJAcZQUTzA9D60XMLgNfa/0/rJxDf+QFo/YBzXg3gvwF8B+/GdBrAHgCnOOfu9qcdAZAemRHGDLRHiqE9Ug7tkaFDe6Qc2iN1hHOPjGYnrpBzPgbABAD3MMbGA3gWQAaAXHgn5n8iOL5IYgMwBsCznPM8AA0AFkZ2SD0K0fzQ+vGh/QjN9QDeiPRYeiIG80PrB0D7xnwDgBEAhgBIgvfvtJ7oPMsfPdAeKYb2SDm0R4YA7ZFyaI80Jpx7ZNQ6cZzzmvZ/jwF4C8AlnPOjnPM2zrkHwPMALonkGCPIEQBHOOc7239eB+8f5KOMsTQAaP/3WITGF2kM54fWTwATAOzlnB9t/5nWjz9+80Prp4OfA/iac36cc+4C8CaAywCkth8dAYChAGoiNcBYgPZIKbRHyqE9MjRoj5RDe6QxYdsjo9KJY4wlMcb6aP8H8AsAB7RfnnZ+DeBAJMYXaTjnPwD4njF2UfulKwEcBLAewLT2a9MAvBOB4UUc0fzQ+gngVvgfg6D144/f/ND66eA7AJcyxhIZYwxn//5sAXBT+3No/XQhtEfKoT1SDu2RIUN7pBzaI40J2x4ZleqUjLGR8H6zCHjD/q9yzv+TMfYSvGFaDuAbALO188mxBmMsF8AqAA4Ah+FVBbIA+DuA8+BdRL+JQDj6AAAAyUlEQVThnJ+M2CAjiGB+VoLWDwCAMZYI4HsAIznnp9uvDQCtHwDC+aG/P+0wxh4BcDMAN4AyADPgPd//OoD+7dcmc85bIjbIXgztkcGhPVIO7ZFyaI+UQ3uknHDtkVHpxBEEQRAEQRAEQcQqUXmckiAIgiAIgiAIIlYhJ44gCIIgCIIgCCKKICeOIAiCIAiCIAgiiiAnjiAIgiAIgiAIIoogJ44gCIIgCIIgCCKKICeOIAiCIAiCIAgiiiAnjiAIgiAIgiAIIor4/6gGxLcXItLRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(121)\n",
    "ax2=plt.subplot(122)\n",
    "ax1.scatter(heightweight[:,1],heightweight[:,2],s=60,alpha=1)\n",
    "discrete_scatter(heightweight[:,1],heightweight[:,2],y,ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
